water Article Spatiotemporal Changes in Temperature and Precipitation in West Africa. Part I: Analysis with the CMIP6 Historical Dataset Gandomè Mayeul Leger Davy Quenum 1,2,*, Francis Nkrumah 1,3 , Nana Ama Browne Klutse 1,4,* and Mouhamadou Bamba Sylla 1 1 African Institute of Mathematical Sciences (AIMS), Sector Remera, Kigali 20093, Rwanda; fraganet92@gmail.com (F.N.); sylla.bamba@aims.ac.rw (M.B.S.) 2 National Institute of Water (NIW), University of Abomey-Calavi, Godomey, Cotonou 01 PB: 4521, Benin 3 Department of Physics, University of Cape Coast, Private Mail Bag, Cape Coast, Ghana 4 Department of Physics, University of Ghana, Legon P.O. Box LG 63, Ghana * Correspondence: davy.gandome@aims.ac.rw (G.M.L.D.Q.); nklutse@ug.edu.gh (N.A.B.K.); Tel.: +229-9606-2003 (G.M.L.D.Q.); +233-2449-83637 (N.A.B.K.) Abstract: Climate variability and change constitute major challenges for Africa, especially West Africa (WA), where an important increase in extreme climate events has been noticed. Therefore, it appears essential to analyze characteristics and trends of some key climatological parameters. Thus, this study addressed spatiotemporal variabilities and trends in regard to temperature and precipitation extremes by using 21 models of the Coupled Model Intercomparison Project version 6 (CMIP6) and 24 extreme indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). First, the CMIP6 variables were evaluated with observations (CHIRPS, CHIRTS, and CRU) of the period 1983–2014;   then, the extreme indices from 1950 to 2014 were computed. The innovative trend analysis (ITA), Sen’s slope, and Mann–Kendall tests were utilized to track down trends in the computed extreme climate Citation: Quenum, G.M.L.D.; indices. Increasing trends were observed for the maxima of daily maximum temperature (TXX) Nkrumah, F.; Klutse, N.A.B.; Sylla, and daily minimum temperature (TXN) as well as the maximum and minimum of the minimum M.B. Spatiotemporal Changes in temperature (TNX and TNN). This upward trend of daily maximum temperature (Tmax) and Temperature and Precipitation in West Africa. Part I: Analysis with the daily minimum temperature (Tmin) was enhanced with a significant increase in warm days/nights CMIP6 Historical Dataset. Water 2021, (TX90p/TN90p) and a significantly decreasing trend in cool days/nights (TX10p/TN10p). The 13, 3506. https://doi.org/10.3390/ precipitation was widely variable over WA, with more than 85% of the total annual water in the study w13243506 domain collected during the monsoon period. An upward trend in consecutive dry days (CDD) and a downward trend in consecutive wet days (CWD) influenced the annual total precipitation on wet Academic Editor: Chang Huang days (PRCPTOT). The results also depicted an upward trend in SDII and R30mm, which, additionally to the trends of CDD and CWD, could be responsible for localized flood-like situations along the Received: 1 November 2021 coastal areas. The study identified the 1970s dryness as well as the slight recovery of the 1990s, which Accepted: 6 December 2021 it indicated occurred in 1992 over West Africa. Published: 8 December 2021 Keywords: extreme climate indices; spatiotemporal variability; innovative trend analysis; Publisher’s Note: MDPI stays neutral Mann–Kendall; Sen’s slope with regard to jurisdictional claims in published maps and institutional affil- iations. 1. Introduction Climate change is a widely known global phenomenon with varying impacts across regions. Its indicator is significant changes in human and natural systems [1,2]. Ref. [3] as- Copyright: © 2021 by the authors. sessed the global surface temperature and found an increase approximately 0.07 ◦C/decade Licensee MDPI, Basel, Switzerland. This article is an open access article during the period 1901–2010 and about 0.17 ◦C per decade from 1979 to 2010. According distributed under the terms and to [4,5], this increase, which is higher in developing countries, is definitely due to both natu- conditions of the Creative Commons ral changes and changes observed in local change inputs (industrialization, transport, etc.). Attribution (CC BY) license (https:// Extreme weather events induce a large range of effects on the environment and society and creativecommons.org/licenses/by/ raise important challenges about environment and resource management for developing 4.0/). countries [6]. Water 2021, 13, 3506. https://doi.org/10.3390/w13243506 https://www.mdpi.com/journal/water Water 2021, 13, 3506 2 of 33 The African continent, according to [7,8], is the second most populous continent in the world. It is also one of the most vulnerable regions to climate variability and change because of its high exposure. Ref. [9] compared the periods 1995–2010 and 1979–1994 and noticed a significant increase in near-surface temperature anomalies over time. With its large latitudinal extent, Africa presents various climatologies, which vary widely. The northern and southern parts of Africa are known as the coolest areas of the continent. Studying the trend of temperature in North Africa, [10] found an increasing trend in the observed annual and seasonal mean surface temperatures. Further studies [11–15] confirmed for 21st-century projections an increase in the mean temperature in North Africa, the Middle East, and the Arabian Peninsula. This notification was in line with the fifth report of the Intergovernmental Panel on Climate Change (IPCC), which stressed that there was an increase in the number of extreme weather events for the 21st century due to climate change [16]. There is a strong link between temperature and precipitation because when temperatures rise, the amount of water vapor in the atmosphere also increases, and the spatiotemporal distributions of precipitation change, resulting in significant differences in precipitation across the world [17,18]. Therefore, obtaining real-time information and facilitating earlier predictions by decision makers can be an efficient tool to adapt and mitigate the impacts of climate events [19,20]. Investing in the present change and potential future change in West Africa (WA) is very useful and helpful because it is a region with unreliable monitoring networks and no or low climatological or meteorological surveys (institutional capacity). Various methods have been applied to investigate the region. They almost all refer to climate scenarios to provide a plausible explanation of how the future may evolve with respect to several variables, including socioeconomic change, technological change, energy and land use, and emissions of greenhouse gasses (GHGs) and air pollutants [21]. The IPCC, in its meeting in 2007, elaborated four levels of emission of trajectories relating to GHG concentrations called the Representative Concentration Pathway (RCP), which was projected by the IPCC in 2014. The RCPs are functions of a possible range of radiative forcing values (2.6, 4.5, 6.0, and 8.5 W/m2) up to the year 2100. On this basis, CMIP5 and CORDEX datasets were introduced into climate science research fields. These datasets, therefore, promoted various studies dedicated to the investigation of climate issues on the basis of the RCP projections. Some previous studies explored the CORDEX dataset [19,20,22–27] to investigate climate variability in Africa and West Africa, while other studies [28–30] examined the performance of the CMIP3/CMIP5 models in simulating extreme climate events. The latest dataset release, from the sixth assessment report of the IPCC, was the Coupled Model Intercomparison Project version 6 (CMIP6). This dataset was driven by a combination of two pathways of forcing, the Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (RCP). WA is one of the most vulnerable regions to climate variability and climate extreme events because of its limited capacity to adapt [8,19]. According to [31], WA, in the future, will be among the regions most highly impacted by climate change. The authors of [31], based on a review of 49 papers, revealed important changes in WA’s climate such as a reduction in the total annual rainfall (in both amount and length) as well as a rise in dry spells during the rainy season. Additionally, they expressed that the WA climate became warmer with important heatwave and spectacular drought episodes. These drought and heatwave episodes have been addressed over the study area by other authors such as those of [32–36]. Refs. [37,38] demonstrated that one of the most important components of the WA’s economy is agriculture, which is primarily rain-fed. The services sector dominates the WA economy, with a contribution of 42% of the GDP, followed by the agricultural sector, which inputs about 35% of the GDP [39]. Agricultural production in WA is uncertain, associated with between- and within- season rainfall variability, and remains a fundamental constraint to many investors, who often overestimate the negative impacts of climate-induced uncertainty [40]. WA has experienced important modifications since the droughts of the 1970s [36,41]. A rise in tem- Water 2021, 13, 3506 3 of 33 perature and modification in rainfall (amount and spell duration) in WA have important implications on agriculture and water resource fields [42–45]. Regarding the projected change in the amounts and frequency of rainfall in WA [19,46,47], the uncertainty is con- siderably high, and several models do not agree on whether the change in rainfall will be negative or positive [48], especially for agriculture production. Ref. [49] explained that various studies on crops productivity over WA have shown that the impact of climate variability on crop yields is more frequently negative than positive. Thus, climate change impacts in the agriculture sector are a major challenge and need to be addressed. Some studies [20,50] have investigated adaptation strategies as a cornerstone to deal with sus- tainable agriculture [51] and climate change impacts [52]. It is important to understand whether the strategies are well designed and account for the best possible understanding of the trends of some parameters (e.g., precipitation and temperature). Using the CMIP6 dataset and referring to scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5, ref. [46] found that CMIP6 projected a continuous and significant increase in the mean annual temperature globally over all of Africa and subsequently over eight subregions of Africa during the 21st century. Meanwhile, ref. [53] adopted the same dataset to assess the representation of daily precipitation characteristics over West Africa and demonstrated how well CMIP6 reproduced observation patterns. However, these studies failed to investigate the trends in or frequency of precipitation and temperatures; they focused only on the variability of the parameters. The present study aimed to use the historical CMIP6 datasets (temperatures and precipitation) and analyze the trends of the changes in daily temperature and pre- cipitation extremes over WA by calculating extreme climate index series, as well as using widely used and innovative statistical methods to assess the levels of changes. Section 2 of this paper, dedicated to the materials and methods, presents the study domain, the various datasets used, and the methodologies adopted, while the results and discussion are presented in Sections 3 and 4, respectively. Finally, the conclusion in Section 5 summarizes the main findings of the study. 2. Materials and Methods 2.1. Study Domain The study area lay in West Africa, which is located between latitudes 0◦ N and 25◦ N and longitudes 25◦ W and 25◦ E (Figure 1). The region is bordered in the South by the Gulf of Guinea and in the north by Mauritania, Mali, and Niger; the Cameroon highlands form the eastern boundary, while the Atlantic Ocean forms the western limit. Rainfall patterns over this region are mostly affected by ocean currents and local features such as topography. In terms of climatic zones, West Africa is divided into three different regions: the Sahel, which is characterized as a semiarid zone ranging from western Senegal to eastern Sudan, between 12◦ N and 20◦ N; the Sudano-Sahelian zone; and the Guinea coast, which is characterized by bimodality driven by the intertropical discontinuity (ITD). 2.2. Data The model dataset utilized for the study was the Coupled Model Intercomparison Project 6th phase (CMIP6) database (precipitation and temperatures), and the observational datasets were CHIRPS, CHIRTS, and CRU. The CMIP6 historical dataset covers the period 1950–2014; CHIRPS covers 1981–present; CHIRTS, 1983–2016, and CRU, 1981–present. 2.2.1. Model CMIP6 Dataset The datasets of the CMIP6 are divided into two phases (historical and projection) for all models used in the study. The datasets were published onto the ESGF data server and were freely accessed by searching the model name together with the reference ID (e.g., experiment name: “historical”, source: “model name”, variable: “pr”, “tasmax”, and “tasmin”) at https://esgf-data.dkrz.de/search/cmip6-dkrz/ (accessed on 18 June 2021), or https://esgfnode.llnl.gov/projects/cmip6/ (accessed on 18 June 2021). Datasets are stored in various formats, but we opted for the NetCDF format, which could be explored Water 2021, 13, 3506 4 of 33 with almost all climate dataset visualization software (NCL, http://www.ncl.ucar.edu/, accessed on 18 June 2021) or Python (https://www.python.org/, accessed on 18 June 2021), R (https://cran.r-project.org/, accessed on 18 June 2021). A historical experiment dataset was available that included data from 1850 to 2014. For climate projection experiments, we used a combination of the Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (RCP), which provide data for the period 2006–2300. In this study, the study period of interest for the calculation of the extreme climate indices was 1981–2014. The rainy season in West Africa is mainly related to the West African Monsoon, which oscillates between May and September (MJJAS) each year. The remaining months of the year were counted as participating in the so-called dry season. The CMIP6 models of Water 2021, 13, x FOR PEER REVIEWin terest have variable spatial resolutions and are illustrated in Table 1. In 4t ooft 3 a4l , we used 21 CMIP6 models that made available both precipitation and temperature. FFiigguurree 11.. SStutuddy ydodmoamina, isnh,oswhinogw Winegst WAfersictaAn ftroipcoagnratpohpyo wgritahp ihtsy thwreieth cliimtsatthicr ezones: Gulf of Guinea (Guinea), Savanna, and Sahel. Source [54]. e climatic zones: Gulf of Guinea (Guinea), Savanna, and Sahel. Source [54]. 2.2. Data 2.2.2. Observed CHIRPS, CHIRTS, and CRU Datasets The model dataset utilized for the study was the Coupled Model Intercomparison ProjeBcte c6atuhs epohfaasel ac(CkMoIfPg6r) oudnatda-bbaases ed(porebcsiepritvaatitoino nadnadt a,tesmcipeenrtaitsutrserse),f erantdo sathte llite datasets aosbasenrvaaltieornnaal tdivateasteotss wuperpel yCHinIRsPitSu, CoHbsIReTrvS,a atinodn Cs.RTUo. Tahded CreMssIPt6h ihsisltiomricitaal tdioatna,seint the present sctouvdeyr,s rthaein pfearlilodd a1t9a50f–r2o0m14t; hCeHCIRlPimS caotveeHrsa 1z9a8r1d–psrGesreonut; pCHInIfRrTaSr,e 1d98S3t–a2t0io16n,s an(Cd HCRIRUP, S) version 2, d1e9v8e1–loppreesdenbt.y the Climate Hazards Group of the University of California, was used (precipi- ta2.t2i.o1n. M: hotdtpels C:/M/IdPa6 tDa.acthasce.ut csb.edu/products/CHIRPS-2.0/africa_daily/, accessed on 2 May 2021)T.hIen dtahteasseatsm oef tlhien eC,MitIsP6c oarrere dsipviodnedd iinntgo ttewmo ppehraasetus (rheis(tmoraicxailm anudm praonjedctmionin) fiomr um) dataset, thalel mColidmelast uesHeda izna trhdes sGturdoyu. pThIne fdraatraesdetsT wemerpe epruabtluisrheewd oitnhtoS tthaeti EoSnGdFa dtaat(aC sHerIvRerT aSn)d, w as adopted (CwHereI RfrTeSel:y[ 3ac7c]e;sTseedm bpye sreaatrucrheinsg: hthtetp m:/o/dedla ntaam.ceh cto.ugectshbe.re wduith/ pthreo dreufecrtesn/cCe IHDI R(eT.gS.,d aily/v1.0/, aecxcpeesrsimedenot nna2meM: a“hyis2to0r2ic1a)l”a, lsoonugrcew: i“tmhotdheel nCalmime”a, tvicariRabelsee: a“rpcrh”, U“tnaistmTaxim”, eanSde ries (CRU: h“tttapsms:/in/”)c raut dhtatptas:./u/esag.fa-dca.utak.d/kcrrzu.d/ed/saetaarc/hh/crmg/ip,6a-dckcrezs/ s(eadcceosnsed2 oMn a1y8 J2u0n2e 12;02p1r)e, coirp itation and tehmttpps:e//reastgufnroeds)e..llnTlh.geovd/partoajseectts/wcmasipu6/s (eadcceastsetdh eond 1a8il yJuntiem 20e2s1t).e Dp.ataTshetes aCreH sItRorPeSd dataset is a qiun avsairgioloubs aflorrmaiantfsa, lbludt awtae soepttcedo vfoerr itnhge N50e◦tCSD–F5 0fo◦rNma, tw, whhiliechC cHouIRldT bSe ceoxvpelorrse6d0 w◦ iSth– 70◦ N. To be caolnmsoisstt enaltl wclimate dataset visualization software (NCL, http://www.ncl.ucar.edu/, accessed on 18it Jhunthe e20s2i1m) uorl aPtyiothnonm (ohtdtpesl:p//werwiowd.sp,ywtheona.dorogp/,t aecdceassCedH oInR 1X8S Ju(wneh 2e0r2e1)X, is indicated foRr (phtrtepcsi:p//citraatni.or-nproorjetcetm.orpge/,r aatcu ◦ ◦ cerses)epd eornio 1d8 oJuf n1e9 82302–12)0. 1A4 huinstdoerirca0l. 2e5xpe×rim0.e2n5t d(a~ta2s5etk m × 25 km) hwoarsiz aovnatilaalbrlee sthoalut tinioclnu.ded data from 1850 to 2014. For climate projection experiments, we uTsehde aC cRomUbainadtioCnH oIf RthPeS Sdharteads eStoscihoaevcoenbomeeinc Pwatihdwelayy u(SsSePd) iandp Rreevpiroesuesntsattuivdei es [19,20,54] aCnodncpenrotrvatiidoend Pastahwtisafya (cRtCoPry), wrehsicpho pnrsoevsidien darteas fpore cthtet poeroiobds e20r0v6a–t2i3o0n0a. Ilnd thaitsa ssteutdsy,( in situ). In ththee pstruedsye npetriwodo rokf ,inbteortehst ofobrs tehrev ceadlcudlaattiaosne otsf t(hCe HexItRrePmSe/ cClimHaItRe TinSdiacnesd wCasR 1U98)1w– ere used to fi2r0s1t4i. nTvhees rtaiginayt esetahseonr eicne Wnterste lAefarsicea (iCs HmIaRinTlyS r[e5l5at]e)di ntor ethsep oWnesset tAofrmicaanx iMmounmsooann, d minimum which oscillates between May and September (MJJAS) each year. The remaining months of the year were counted as participating in the so-called dry season. The CMIP6 models of interest have variable spatial resolutions and are illustrated in Table 1. In total, we used 21 CMIP6 models that made available both precipitation and temperature. Water 2021, 13, 3506 5 of 33 temperatures and then to see whether, as CHIRPS fits well with CRU (precipitation) CHIRTS could be a response to CRU (temperature). 2.3. Methods Several studies have been conducted to date by scientists in designing methods and ap- plications for the computation and analysis of extreme climate indices for better monitoring and tracking of climate trends. The methodology adopted in this study consisted of: i. analysis of the precipitation and the temperature recorded in the observational dataset; ii. computation of the selected climate indices; iii. analysis of the spatial trends of the extreme indices computed with modified Mann– Kendall and Sen’s slope tests; iv. assessment of the performance of a new method (innovative trend analysis: ITA) in respect to that of the methods in iii.; v. analysis of the temporal variability and trend of the computed indices. 2.3.1. Selected Extreme Climate Event The climate indices agreed with the international committee of the Expert Team on Climate Change Detection and Indices (ETCCDI), which aims to provide a good mixture of daily statistics to assess changes in temperature and precipitation regimes in terms of duration, intensity, and occurrence [56,57]. Based on the combination of CDO com- mands and the Climpact (https://climpact-sci.org/indices/, accessed on 13 May 2021), the extreme indices listed in Tables 2 and 3 were calculated. The computed indices were classified into 5 sets of extremes climate indices: (i) absolute extreme indices—precipitation: RXnday (n = 1, n = 5, and n = 7), temperature: TXX, TXM, TMM, TNX, TNN, TNM, TXN; (ii) threshold exceedance indices such as R10mm, R20mm, and R30mm, which refer to the number of days on which a threshold was surpassed; (iii) indices that highlight the length of wet and dry spell duration, for instance, CDD and CWD; (iv) percentile-based precipitation and temperature indices (R95p, R99p, TN10p, TX10p, TN90p, TX90p, and WSDI); and (v) other indices—SDII and PRCPTOT. 2.3.2. Criteria of the Analysis of the Extremes Some basic criteria were set to be accounted for in the analysis of the trend of each extreme climate event: (a) a minimum of 80% (17 models of 21) of the CMIP6 models needed to reflect the events or the trends; (b) a significant trend had to be demonstrated by at least 80% of the models. 2.3.3. Statistics Constraints to Evaluate the Performance of Models The correlation analysis method was employed to evaluate the relationship between models and observations on one hand, and between indices on the other hand. Additionally, the nonparametric Mann–Kendall (MK) trend test [58,59], which is strongly recommended in this kind of analysis by the World Meteorological Organization (WMO), was adopted to evaluate the trends in extreme climate (both precipitation and temperature) indices. It was jointly associated with Sen’s slope test [60] for better appreciation of the trend. The aforementioned tests were all based on hypotheses. The null hypothesis (Ho) assumed that the data were independent and randomly distributed, and the alternative hypothesis (H1) supposed that there was a monotonic trend in the dataset. The utility of these two tests was that the MK test (with its Z Kendall coefficient) provided an idea about the significance of the trend, while Sen’s test (with the slope estimator) estimated the trend magnitude. The assessment was also based on a chosen 95% threshold of confidence level. An innovative trend analysis (ITA) was applied to detect monotonic trends and subtrends in the time-series dataset. This methodological approach was used to detect the annual and seasonal trends. Water 2021, 13, 3506 6 of 33 • Mann–Kendall test (MK) MK [58,59] is one of the common formulas used in hydrology and meteorology to identify trends in a time-series dataset. The test statistic (S) of series (x1, x2, x3, . . . and xn) for which the trends are being checked can be expressed with the following formula: n−1 n ( ) S = ∑ ∑ sign xj − xk (1) k=1 j=k+1 where n is the length of the dataset and xj and xk indicate the observations at times j and k.( )  +1 i f xj > xk Sign xj − xk =  0 i f xj = xk (2)−1 i f xj < xk A positive value for S indicates an increasing or upward trend, while a negative value shows a decreasing or downward trend in the time-series data. The variance of S, VAR(S{), can be calculated by the equation: } 1 ρ VAR(S) = n(n− 1)(2n + 5)−∑ τi(τi − 1)(2τ18 i + 5) (3)i=1 where ρ denotes the tied group number of observations in group I, which is a set of sample data with similar values, and i indicates the extent of the ith tie number. The time-series data statistic (S) is identified with Kendall’s τ (tau), which is given as follows: S √ τ = 1 ( ) √ Bwith, B = 2 n(n− 1)− 1 g2 ∑j=1 ρj ρj − 1 12 n(n− 1). Through the estimation of S and the variance VAR(S), the standardized test measure- ment Z is as follows when n > 10 [61]:  √ S−1 , i f S > 0 VAR(S)Z =  0, i f S = 0 (4)√ S+1 , i f S < 0 VAR(S) The positive (+) values of Z indicate an increasing trend, and the negative (−) values depict a decreasing trend. • Modified Mann–Kendall test (MMK) From [62], modified VAR(S) stat(istics can be estima)te(d wit)h the equation: n(n− 1)(2n + 5) n VAR(S) = . (5) ( ) 18 n∗e Here, the correction factor nn∗ is adjusted to the autocorrelated data as:( ) ( e ) n 2 n−1 ∗ = 1 + 3 − (n− f )(n− f − 1)(n− f − 2)ρ ( f ) (6)ne n 3n2 + 2n ∑ e f=1 ρe( f ) signifies the autocorrelation between ra(nk)s of observations and can be estimated as:π ρ( f ) = 2sin ρ ( f ) (7) 6 e Water 2021, 13, 3506 7 of 33 • Sen’s Slope Estimator The Sen’s slope (Q) is the median of N values of Qi [63], where xk − xQ ji = − , i = 1, 2, 3, . . . , N, k > j (8)k j Thus if some zero values of Qi fall in between equal numbers of negative and positive values of Qi, the Sen’s slope (Q) is zero. The greater the number of equal values there are in a time series, the higher the probability of a no-change trend for the series [64]. • Innovative Trend Analysis (ITA) One recent method proposed for hydroclimatic variability assessment is innovative trend analysis (ITA), which was first elaborated in [65]. This new and robust technique is used in hydrometeorology for trend detection. It has been applied for different variables such as groundwater, rainfall, temperature, and evapotranspiration [66,67]. The methodol- ogy consists of dividing into two subseries of equal numbers in the observation variables of the study. For the next step, each subseries is reorganized in ascending order and plotted against the other in a Cartesian coordinate system [68]. The first half is plotted on the X-axis, and the second half on the Y-axis. After that, a straight line is fitted with the scatter plot that represents the “monotonic trend “or so-called “no trend”. When the scatter is concentrated above the 1:1 line, the time series has an increasing trend, and if the scatter points concentrate below the line (1:1), a decreasing trend in the time series is indicated [69]. The trend indicator of ITA [65] is calculated fro(m the fo)llowing equation: 1 n 10 x B ∑ j − xk = (9) n i=1 x where B represents the ITA slope, n denotes the extent of individual subseries, xj and xk represent the values of the consecutive subseries, and x represents the mean of the first subseries (xk). Table 1. Reference of CMIP6 dataset used in this study. N◦ Models Institute Horizontal Resolution References 1 ACCESS-CM2 Commonwealth Scientific and Industrial Research Organization, ◦ ◦Australia Bureau of Meteorology (BoM), Australia 1.9 × 1.3 [70] 2 ACCESS-ESM1-5 Commonwealth Scientific and Industrial Research ◦ ◦Organization, Australia 1.9 × 1.2 [71] 3 AWI-ESM-1-1-LR Alfred Wegener Institute, Helmholtz Centre for Polar and Marine ◦ ◦Research, Germany 1.9 × 1.9 [72] 4 BCC_ESM1 Beijing Climate Centre (BCC) and China Meteorological ◦ ◦Administration (CMA), China 2.8 × 2.8 [73] 5 CanESM5 Canadian Earth System Model, Canada 2.8◦ × 2.8◦ [74] 6 EC_EARTH3-VEG-LR EC—Earth Consortium, Rossby Center, Swedish Meteorological ◦ ◦and Hydrological Institute (SMHI), Sweden 0.7 × 0.7 Not available 7 EC_EARTH3-CC EC—Earth Consortium, Rossby Center, SMHI, Sweden 0.7◦× 0.7◦ [75] 8 FGOALS_f3_L LASG, Institute of Atmospheric Physics, Chinese Academy of ◦ ◦Sciences and CESS, Tsinghua University, China 1.3 × 1.0 [76] 9 FGOALS_g3 LASG, Institute of Atmospheric Physics, Chinese Academy of ◦ ◦Sciences and CESS, Tsinghua University, China 2.0 × 2.3 [77] 10 IPSL-CM6A-LR Institut Pierre-Simon Laplace (IPSL), France 2.5◦ × 1.3◦ [78] Japan Agency for Marine–Earth Science and Technology; 11 MIROC6 Atmosphere and Ocean Research Institute (University of Tokyo); 1.4◦ × 1.4◦ [79] and National Institute for Environmental Studies, Japan 12 MPI-ESM-1-2-HAM Max Planck Institute for Meteorology, Germany 1.9◦ × 1.9◦ [80] 13 MPI_ESM1_2_HR Max Planck Institute for Meteorology, Germany 0.9◦ × 0.9◦ [81] 14 MPI_ESM1_2_LR Max Planck Institute for Meteorology, Germany 1.9◦ × 1.9◦ [82] 15 MRI_ESM2_0 Meteorological Research Institute (MRI), Japan 1.1◦ × 1.1◦ [83] 16 NESM3 Nanjing University of Information Science and Technology, China 1.9◦ × 1.9◦ [84] 17 NorCPM1 NorESM Climate modeling Consortium consisting, Norway 2.5◦ × 1.9◦ [85] 18 NorESM2_MM Norwegian Climate Center, Norway 1.3◦ × 0.9◦ [86] 19 NorESM2-LM Norwegian Climate Center, Norway [87] 20 SAMO_UNICON Seoul National University Atmosphere Model Version 0 with a ◦ ◦Unified Convection Scheme, South Korea 1.2 × 0.9 [88] 21 TaiESM1 Research Center for Environmental Changes (AS-RCEC), Taiwan 0.9◦ × 1.3◦ [89] Water 2021, 13, 3506 8 of 33 Table 2. Precipitation extreme indices. Index Description Name Definition Units R95p Very wet day precipitation Annual total precipitation when RR > 95th percentile mm R99p Extremely wet day precipitation Annual total precipitation when RR > 99th percentile mm Rx1day Maximum 1-day precipitation Annual maximum 1-day precipitation mm Rx5day Maximum 5-day precipitation Annual maximum consecutive 5-day precipitation mm Rx7day Maximum 7-day precipitation Annual maximum consecutive 7-day precipitation mm PRCPTOT Wet day precipitation Annual total precipitation on wet days mm SDII Simple daily intensity index Average precipitation on wet days mm/day CDD Consecutive dry days Maximum number of consecutive dry days day CWD Consecutive wet days Maximum number of consecutive wet days day R10mm Number of heavy precipitation days Annual count of days when RR > 10 mm day R20mm Number of very heavy precipitation days Annual count of days when RR > 20 mm day R30mm Number of heaviest precipitation days Annual count of days when RR > 30 mm day Table 3. Temperature extreme indices. Index Description Name Definition Units TXM Annual mean TX Arithmetic mean of the monthly mean value of TX ◦C TNM Annual mean TN Arithmetic mean of the monthly mean value of TN ◦C TXX The maximum value of TX Highest TX in a year ◦C TNX The maximum value of TN Highest TN in a year ◦C TXN The minimum value of TX Lowest TX in a year ◦C TNN The minimum value of TN Lowest TN in a year ◦C TN10p Cold nights Percentage of days when TN < 10th percentile % TX10p Cold days Percentage of days when TX < 10th percentile % TN90p Warm nights Percentage of days when TN > 90th percentile % TX90p Warm days Percentage of days when TX > 90th percentile % WSDI Warm spell duration index Annual count of when at least six consecutive daysof maximum temperature >90th percentile day CSDI Cold spell duration index Annual count of when at least six consecutive daysof minimum temperature < 10th percentile day A positive slope of the B value indicates an increasing trend in the series, whereas a negative value of the slope signifies a decreasing tendency in the time series. 3. Results This section focuses on presenting the main findings of the study. It depicts rainfall and temperature variabilities using selected climate indices over the study area and reports our analysis of their various trends with statistical tests. The reader is invited to see the supplementary documentation for additional figures and tables that are not shown here. 3.1. Scenario Models Validation Validation of the CMIP6 models used was undertaken with observed rainfall and temperature (maximum and minimum) for the two databases in the period 1983–2014. 3.1.1. Rainfall Evaluation Figure 2 shows the interannual distribution of rainfall averages for both the CMIP6 (21 models) and observed (CRU and CHIRPS) data as well as for the ensemble mean of the models. It can be seen that the total annual rainfall varied greatly across the study domain. For all the models and observations, there existed a northward gradient from higher to lower values, with the highest values recorded over the Guinea Highlands and Cameroon mountains (about 2500 mm/year for the observed and 2000 mm/year for the model ensemble mean). The Savannah region was generally wet, with a rainfall of about 700 mm/year. For the northern part, the rainfall recorded was less than 400 mm/year, and for the southern part (around the Guinea Coast), it was about 1100 mm to 1300 mm per year. Some models (ASSECC-ESM1-5, EC-Earth3-Veg, EC-Earth3-CC, FGOAL-g3, etc.) underes- timated the rainfall compared with both observations (CHIRPS and CRU) and the model ensemble average. Other models (MPI-ESM-1-2-HAM, FGOAL-g3, MRI-ESM2-0, etc.) Water 2021, 13, 3506 9 of 33 Water 2021, 13, x FOR PEER REVIEW 10 of 34 overestimated (especially in regions such as Cameroon’s mounts and Gabon’s forests) the observed rainfall datasets. Figure 2. Spatial distribution of the annual mean rainfall from observed data (CHIRPS and CRU) and the selected CMIP6 dattasseett oveerr tthee peerriiod 1981–2014.. TThhee rraaiinnffaallll rreeccoorrddeedd dduurriinngg ththee mmoonnsosooonn ppereiroiodd (M(MJJJAJAS)S o) voevre trhteh setustduyd dyodmoamina iins disedpeicpteicdt eidn insuspupplpemleemnetanrtyar ymmatearteiarli a(lF(iFgiugruer eS2S)2. ).AAs sinin ththee ccaassee ooff tthhee aannnnuuaall rraaiinnffaallll aavveerraaggee,, aa nnoorrtthhwwaarrdd ggrraaddiieenntt wwaass nnootteedd,, wwiitthh tthhee mmaaxxiimmuumm aanndd mmiinniimmuumm ooff rraaiinnffaallll rreeccoorrddeedd oovveerr tthhee ssaammee llooccaattiioonnss.. AA hhiigghh ccoorrrreellaattiioonn ooff mmooddeellss wwiitthh oobbsseerrvvaattiioonnss wwaass nnootteedd aatt llaattiittuudd ◦ ◦ ee 55° NN––2200° NN.. TThhee mmaaxxiimmuumm ccuummuullaattiivvee rraaiinnffaallll rreeggiisstteerreedd dduurriinngg tthhee mmoonnssoooonn ppeerriioodd wwaass aabboouutt 22000000 mmmm//yyeeaarr.. FFiigguurree 33 pprreesseennttss tthhee aavveerraaggee ppeerrcceennttaaggee ooff tthhee ccoonnttrriibbuuttiioonn ooff tthhee mmoonnssoooonn ttoo tthhee aannnnuuaall ttoottaall mmeeaann.. TThhee mmoonnssoooonn ccoonnttrriibbuutteedd ssiiggnniiffiiccaannttllyy ttoo tthhee ttoottaall rraaiinnffaallll iinn tthhee SSaavvaannnnaahh rreeggiioonn ((eevveenn uunnddereersetsitmimataitning gmmodoedlesl csocnofnirfimrmede tdhitsh oisbosebrsveartvioatni)o. nB)e. twBeetewne tehn◦ ◦ e ltahteituladtietsu d5°e sN5 anNd 2a0n°d N2,0 atN le,aastt l8e5a%st o8f5 %theo rfatihnefarlal ianmfaolluanmt wouasn trewceaisvreedc ediuvreidngd uMrainyg– SMeapyte–mSebpetre.m Itb ewr.aIst walasos aolbsoseorbvseedr vtehdatt htahtet hWeeWste sAtfArifcraicna nMMonosnosoono nddidid nnoot tccoonnttrriibbuuttee ttoo rraaiinnffaallll aabboovvee ◦ 2255° NN oorr bbeellooww tthhee eeqquuaattoorr.. 3.1.2. Temperatures Evaluation The assessment of the maximum and minimum temperatures (Tmax and Tmin re- spectively) revealed that, for observations as well as models, the temperatures varied Water 2021, 13, 3506 10 of 33 widely. Models differently represented the temperature, both for Tmax (Figure 4) and Tmin (Figure S2). All the minima of Tmin were located in the northern part, especially in the northeastern area, while the maxima of the Tmin were captured in the western region; the other parts were, on average, 22 ± 3 ◦C. For the Tmax, some models underestimated the observations (NESM3, SAMO-UNICON, IPSL-CM6A-LR, etc.) while others overesti- mated (MIROC6, CanESM5, BCC-ESM1). The lowest values of Tmax were located in the northeastern regions (as in the case of the minimum of the Tmin) and scattered around Cameroon’s mountains and Gabon’s forests. Each model, whether over- or underestimat- ing, agreed that the hottest area was the Savannah’s band, located at the borders of Mali, Mauritania, and Senegal. The models’ ensemble mean was closer to the annual average of Water 2021, 13, x FOR PEER REVIEW CRU than CHIRPS and depicted the observations better than the individual models. This11 of 34 means that the models’ ensemble provided a better estimate of the parameters than the individual models. FigureF ig3u. rSep3a.tiSapl adtiiasltrdibisutrtiibount ionf tohfet hpeeprceercnetnatgaeg eofo fththee ccoonnttriibutiion oofft htheeW WesetsAt fArifcraincaMn oMnsoonosnotoont htoe atnhne uaanl nrauianlf arlal infall averagavee oravgeer othver ptheeripoedr i1o9d819–8210–1240 1b4abseasde dono nththe eoobbsseerrvveed data ((CHHIRIRPPSSa nadndC RCUR)Ua)n danthde tsheele sceteledcCteMd ICP6MdIaPt6as deta.taset. 3.1.2. Temperatures Evaluation The assessment of the maximum and minimum temperatures (Tmax and Tmin respectively) revealed that, for observations as well as models, the temperatures varied widely. Models differently represented the temperature, both for Tmax (Figure 4) and Tmin (Figure S2). All the minima of Tmin were located in the northern part, especially in the northeastern area, while the maxima of the Tmin were captured in the western region; the other parts were, on average, 22 ± 3 °C. For the Tmax, some models underestimated the observations (NESM3, SAMO-UNICON, IPSL-CM6A-LR, etc.) while others overestimated (MIROC6, CanESM5, BCC-ESM1). The lowest values of Tmax were located in the northeastern regions (as in the case of the minimum of the Tmin) and scattered around Cameroon’s mountains and Gabon’s forests. Each model, whether over- or underestimating, agreed that the hottest area was the Savannah’s band, located at the borders of Mali, Mauritania, and Senegal. The models’ ensemble mean was closer to the annual average of CRU than CHIRPS and depicted the observations better than the individual models. This means that the models’ ensemble provided a better estimate of the parameters than the individual models. Water W20a2te1r, 21032,1 x, 1F3O, 3R5 0P6EER REVIEW 11 of 3312 of 34 FigurFei g4u. rSep4a.tSiapla dtiiasltdriibsturitbiounti oonf tohfeth me amxaixmimuumm tetemmppeerraattuurree oovveerrt htheep pereiroidod19 18918–210–1240b1a4s bedasoendt ohne othbsee orvbesderdvaetda (dCaHtaIR (PCSHIRPS and CanRdUC) RaUnd) atnhde tsheelescetleecdt eCdMCIMPI6P d6 adtaatsaeste.t . 3.23.. 2In. tIenrtaernannunaula lRRaaininffaallll aanndd TTeemmppereartautruerseTsr Tenrdend To study the trends in the selected CMIP6 models, the work investigated the historical perTiood s1t9u5d0–y2 0t1h4e, ftorrewndhsic hinb otthhep rseecliepcitteatdio nCaMnIdPt6e mmpoerdaetlusr,e tdhaet awwoerrek aivnavileasbtlieg.aTtoed the hisatsosreiscsatlh epeanrinouda l1r9a5in0f–a2ll0a1n4d, tfeomr pwerhaitcuhre btroetnhd ,parnecaidpviatantcieodng aranpdh itceaml mpeetrhaotduroef tdreantda were avdaieltaebctlieo.n Twoa assaspepslsi etdhteh atninuclauld readininfnaollv atnivde teremnpdearnaatluyrseis t(rITeAnd),, thaen maoddviafinedceMd agnrna–phical meKtehnoda lolft etsrten(MdM dKet)e,catniodnS ewna’sss alopppel.ied that included innovative trend analysis (ITA), the modified Mann–Kendall test (MMK), and Sen’s slope. 3.2.1. Trend of Annual Rainfall 3.2.1. TFriegnudre o5f, Adenpniuctainl gRtahienfeavlal luation with the MMK test, shows, based on its corrected Z statistic (Zc) that the trend of the rainfall varied from one model to another. From FigFuirgeu5rae– u5,, tdheepriecdticnrgo stshien deivcaatluesawtiohner ewtirtehn dthsew MerMe sKig nteifisct,a nsht oatw9s9,% bacosendfid oenn cietsl ecvoerlrected Z s(CtaLti)s, taincd (Zthce) bthlaactk thcreo strseinndi coaft ethsew rhaeirnefatrleln vdasrwieedr efrsoigmn iofincaen mt aotdaet l9 t5o% aCnLo.thGelor.b Falrloy,mal lFigure 5a–thue, mthoed reelsdi ncdroicsast eidndsigcnatifiesc awnth(eriteh etre9n5d%s owr e9r9e% soigf nCiLfi)cpaonsti taivt e9t9r%en dcos;nnfoidnesingcnei filecavnetl (CL), and the black cross indicates where trends were significant at at 95% CL. Globally, all the models indicated significant (either 95% or 99% of CL) positive trends; nonsignificant negative trends of annual rainfall were noticed over the Guinea Highlands (reduction of about 4–5 mm/decade) and some other parts, such as Cameroon. Water 2021, 13, 3506 12 of 33 Water 2021, 13, x FOR PEER REVIEW 13 of 34 negative trends of annual rainfall were noticed over the Guinea Highlands (reduction of about 4–5 mm/decade) and some other parts, such as Cameroon. FigurFei g5u. Srep5a.tiSapla dtiiasltrdiibsutrtiibounti oonf tohfet haenannunaula rlarianinfafalll ltrtreennd ooveerr ttheep peeriroiodd1 915905–02–021041b4a sbeadseodn tohne tChMe ICPM6 dIPat6a sdeat.taAsneat.l yAsinsalysis basedb aosne dthoen mthoedmiofideidfi eMd aMnann–nK–Kenenddaalll l((MMK)) tteesst.t.T Thehere rdecdr ocsrsoesss(e+s) i(n+d) iicnadteicaareteas awreitahs swigintihfi csaingtntirfeincadnsta ttr9e9n%dCs La,ta 9n9d% CL, and ththee bbllaacck ccrrosssseess( +(+))i ninddiciactaetaer aearesaws iwthisthig nsiigfincainfitctarnent dtrseantd9s5 %at C9L5%. CL. ThTeh SeaSvaavnannnahah rreeggiioonn eexxppeerriieenncceedda as isginginfiicfaicnatnint cirnecarseeaisnea innn uanalnruaainl fraalli.nIfnalml. oIsnt most locations in the study domain, a significant decreasing trend in annual rainfall was evident locoavtieornCsa mine rtohoen astnuddGya bdoonm, paainrt,i cau lasrilgyniinfimcaondte ldsescurcehaassinAgW tIr-eEnSdM -i1n- 1-aLnRn,uBaCl Cr-aEiSnMfa1l,l was eviNdeonrCt PoMve1r; oCthaemremrooodnel sanshdo wGeadboann, inpcarretaicsue lianrltyh einy emarolyderalsin sfaulclhfo arst hAesWe Ia-rEeSasM. -I1n-1-LR, BCFCig-EuSreM51v,, tNheobrCluPeMcr1os; soetshienrd imcaotedwelhse srehoatwleeads ta8n0 %inocfrethaesem iond ethlsed yepeiacrtleyd raatirnefnadllw foitrh these are9a9s%. ICnL F, iwghuirle i5nvF, itghuer eb5luwe, tchreobslsueesc irnodssiecsaetex pwrehsesrwe haetr eleaatslte a8s0t%80 o%f otfhme omdoeldsedlesp dicetpedicted a treandtr ewnidthw 9it9h%9 5C%LC, wL.hTihlee isnu bFpigloutsreF i5gwur, eth5ev, wbluined cicraotsesdesg leoxbpalrleystsh wat,hfeorre tahte lCeMasItP 860% of momdoedlse ldsesptuicdtieedd, ath tereSnavda wnniathh s9h5o%w CedLa. Tmhaex ismuubmplroattse Foifgausrieg n5ivfi,cwan itnindcirceaatseedi nglaonbnaulalyl that, forr athinef aCllM(aIbPo6u tm4–o5dmelms /stduedcaiedde),. tAhes eSvaevreadnencalihn esh(noownseidg naifi mcaanxt)imwuasmid reantteifi oedf aa rsoiugnndificant inctrheeaGseu iinne aanHniugahll arnadinsfbaullt (waabsoruetv 4ea–l5e dmbmy /ldesesctahdaen).8 A0% soefvtehree cdoencsliidneer e(dnomnosdigelnsi.ficant) was identified around the Guinea Highlands but was revealed by less than 80% of the considered models. The same trends were observed from the results of Sen’s slope test (Figure 6) as from the MMK results (Figure 5). The trends were located in the same areas and differed only in magnitude. The yearly reduction in rainfall around the Guinea highlands here was about 0.2–0.5 mm, while the global increase noted reduced by 5 mm/decade. The yearly variability (average increasing) of the rainfall over West Africa was about 3 mm/decade, Water 2021, 13, 3506 13 of 33 The same trends were observed from the results of Sen’s slope test (Figure 6) as from Water 2021, 13, x FOR PEER REVIEW the MMK results (Figure 5). The trends were located in the same areas and differed only14 of 34 in magnitude. The yearly reduction in rainfall around the Guinea highlands here was about 0.2–0.5 mm, while the global increase noted reduced by 5 mm/decade. The yearly variability (average increasing) of the rainfall over West Africa was about 3 mm/decade, bubt uatroauronudn dthteh eSaSvaavnannnahah, ,SSeenn’s’s ssloloppee iinnddiiccaatteedd aa ssiiggnniifificcaannttu uppwwaradrdt rternedndo foaf baobuotut 4.5 mm4./5dmecma/ddee ocfa daet loefaastt laeat s9t5a%t 9 C5%L CuLnduenrd ae rma imniimnimumum ofo f8800%% ooff tthee ssttudiieeddm mooddelesl.s. FigFuirgeu r6e. 6S.aSmame aesa sFiFgiguurere 55,, bbutt tthe annaalylyssisisi sisb absaesdedon oSne nS’esns’lso psleotpeset .test. ThTeh eeveavlauluaatitoionn ooff tthhee aannnnuuaal lr arianifnafllatlrle tnrdenbdy bapyp alypipnlgytihnegI TthAei sITpAre siesn pterdesiennFtiegdu rien 7F.igure In Figure 7a–u, the red crosses (+) express increasing trends, and the blue minuses (−) 7. Idne cFriegausirneg 7tare–nud, st.hTeh reesdp actrioasl sdeisst r(i+b)u etixopnreosfst hiencITreAatsrienngd tirnednidcast,o arnddis pthlaey bedluteh emsianmueses (−) decvraeraiasbinilgit iterseanndds.t rTehnde sspasadtiiadl Sdeinst’rsisbluoptieon(F oigfu trhee6 I)TaAnd trMenMdK in(Fdigicuarteo5r) dtrisepndlaayneadl ytshies. same varOianboilnietiehsa nadn,dd tercelinndinsg atsr ednids Sceirnc’usl asltoedpeo v(Ferigtuheree x6t)r eamnde wMeMsteKr n(Fpiagrut roef W5) Atr(eGnudi naenaalysis. Onh iognhela hndasn)da,n dewcleirneisnhgo wtrnenbdysa tclieracsutl8a0te%do of vtheer mthoed elxs.trOenmteh ewoethseterrhna npda,ratb ofu tW60A% (oGfuinea higthelamndods)e lasnrdev weaelerde sthaotwthne bCya mate rleoaosnt m80o%un otafi nthsea nmdoGdaeblso.n Ofonr etshtes eoxthiebri thedanthde, asabmouet 60% of dthecel imninogdetrlse nrdesv.eGaleende rtahllayt, tthhee rCesatmoferthoeonW Amoduomntaaiinnse xapnedri eGncaebdona sftoarbelsetisn cerxehaisbinitged the same declining trends. Generally, the rest of the WA domain experienced a stable increasing trend. The trends highlighted were all significant, at least at 95% CL. Additionally, a few significant trends ignored by MMK and Sen’s slope could be identified here, justifying the reliability of ITA to track unseen trends in time-series data. Furthermore, the identified trends from Sen’s slope and MMK were increased in magnitude under ITA. The maximum amount of the trend for ITA was about 10 mm/decade and was very high between the latitudes of 5° N and 15° N. In the northern part of WA, the trend indicator expressed a decrease of about +2 mm/decade. Water 2021, 13, 3506 14 of 33 trend. The trends highlighted were all significant, at least at 95% CL. Additionally, a few significant trends ignored by MMK and Sen’s slope could be identified here, justifying the reliability of ITA to track unseen trends in time-series data. Furthermore, the identified trends from Sen’s slope and MMK were increased in magnitude under ITA. The maximum Water 2021, 13, x FOR PEER REVIEW amount of the trend for ITA was about 10 mm/decade and was very high between th1e5 of 34 latitudes of 5◦ N and 15◦ N. In the northern part of WA, the trend indicator expressed a decrease of about +2 mm/decade. FigureF i7g.u rSep7a.tSiapla tdiailsdtriisbtruibtiuotnio noof f tthhee aannnnuaularla irnafainllftarleln dtreonvedr tohveepre rtihode 1p9e50ri–o2d01 419b5as0e–d20on14t hbeaCsMedIP 6ond attahsee t:CaMsseIPss6m denattaset: assesswmiethntt hweiitnhn tohvea tiinvneotrveantdivaen atrlyensids (aITnAal)ytseisst. (ITA) test. 3.2.2. Trend of Maximum and Minimum Temperatures 3.2.2. TrFeingdu roefs M8 aanxdim9 udmisp alanydt rMenidniamnaulmys eTseomf tpheermatauxriems um temperatures of the selected CFMigIPu6redsa 8ta asentdo v9 edritshpelapye rtiroednd19 a5n0–a2ly0s1e4sb oafs etdheo nmSaexnim’s uslmop teemanpdetrhaetuMreasn no–f Ktheen dsaelllected CMtIePs6t, dreastpaescetti voevlyer( Ftihgeu rpeserSi3o–dS 619d5e0p–ic2t0t1h4e bSaense’sds loonp eSoefnT’sm silno,pteh eaMndM tKheo fMTamnin–, Ktheendall testI, TrAespofecTtmivaexl,ya (nFdigtuhreeIsT SA3o–fS6T mdeinp,icret stpheec Stievnel’ys )s.loItpwe aosf pTemrcieni,v tehdet MhaMt tKhe oifn Ttemrainnn, uthael ITA of Tvmariaaxb,i liatnieds otfhTem IiTnAa nodf TTmmaxinw, erreespsiegcntiifivcealnyt).a tItl ewasat sa tp9e5r%ceCivLe(dre dthcarto stshees iinndtiecraatennual varisaigbniliifiticeasn totfr eTnmdsina ta9n9d% TCmL,aaxn dwbelraec ksicgronsisfiecsatnhto saet altea9s5t% aCt L9)5f%or CalLl m(roedde lcsr.osses indicate significant trends at 99% CL, and black crosses those at 95% CL) for all models. Water 2W02at1e,r 1230,2 x1 ,F1O3,R3 5P0E6ER REVIEW 15 of 3136 of 34 FiguFirgeu 8r.e S8a. mSaem aesa FsiFgiugruer e66, ,bbuutt ffoorr tthee maaxxiimmuummt etmempepreartuarteur(Te m(Tamx).ax). The Z statistic score, the Sen’s slope, and the ITA slope, as well as the latter’s indicator, exTphrees sZh oswtatmisuticch stchoerceh, atnhgee sShenav’se ismloppaec,t eadnWd Ath. eF oIrTtAhe sSlaovpaen, nash wregeliol nass, atlhmeo slat tter’s ind9ic0a%toorf, mexopdreelsss howe dmcuhcahn gtheet rcehnadns,gbeus thnaovtew imithptahcetesdam WeAm.a Fgonrit tuhdee ;SoanvlaynNnaEhS Mre3g, ions, almMoPstI -E90SM% -1o-f2 -mHoAdMe,lsa nsdhoMwPeI-dE ScMha1-n2g-He Rtrbeenhdasv,e bdudti fnfeoret nwtlyithu ntdheer tshaemSee nm’saagnndiMtuMdeK; only NEtSeMsts3.,F MorPIIT-AE,ShMow-1e-2v-eHr, AthMe t,r eannddw MasPdI-yEnSaMm1ic-a2l-lHy Rel abbeohraatveedd, sdhiofwfeirnegn,tfloyr uinnsdtaenrc eth, ien Sen’s andth Me cMasKe otfetshtse. aFfoorre mITeAn,t ihonoewdemveord,e tlhs,eo tnreonnde hwanads ,daycnleaamr iinccarlelyas einlagbtorernadte(dn,o srthhowwairndg, for from Savannah: 0.6 mm/year), and on another hand, a southward decreasing trend for instbaonthceT, minin thaen dcaTsme aoxf. the aforementioned models, on one hand, a clear increasing trend (northwGaernde frraollmy, fSoarveaitnhnearhT:m 0a.6x mormT/myeina,rt)h, earnedw oans aansoigtnhiefirc hanatnidn,c are saosiuntghwtreanrdd adcercorsesasing trenWdA fowri tbhoathn Tormthiwn aarnddg Tramdiaexn.t . The northern part grew hotter than the central and the soGutehneernraplalyrt,s .foITrA eirtehvera lTedm(aFxig uore T9vm) itnh,a tthalelrteh ewmaosd ae lsi(grendifcircoasnstl oicnactrioenas)inidge ntrtiefined across WAp awrtiicthul ar ninocrrtehawsinagrdtr egnrdadiniethnet.n Tohrteh enronrptahretrann dpathret Ggrueinwe ah-cootatestr. tThhaenin tchrea csienngtrtraeln adnsd the souatrhoeurnnd ptahretsG. uITinAea rceovaesatlceodu (lFdibgeudreu 9evto) tthhaetg arlald tuhael minocrdeealsse (irnedse carsousrsf alocecatetimonpse)r aidtuernetified a p(aSrStTic)udluaer tiongcrloebaaslinwga rtmreinndg [i9n0 ]t.he northern part and the Guinea-coast. The increasing trends around the Guinea coast could be due to the gradual increase in sea surface temperature (SST) due to global warming [90]. Water 2W0a2t1er, 12032, 1x, F13O, R35 P06EER REVIEW 16 of 3317 of 34 FiguFrieg u9.r eSa9.mSea mase FaisgFuigreu r6e, 6b,ubtu tththe eaannaallyyssiiss iiss basseeddo onnt htheem modoidfiiefdieMd aMnna–nKne–nKdeanlld(aMllM (MK)MteKst). test. 3.3. Spatial Changes in Temperature Indices 3.33. .S3.p1a.tPiaelr cCehnatnileg-eBs aisne dTeTmempepreartautruer eInIdnidciecse s (TN10p, TX10p, TN90p, TX90p, and WSDI) 3.3.1. PFerigcuenreti1l0e-sBhaosweds tTheemsppaetiraaltduirsetr Iibnudtiicoens o(fTtNhe10copl,d TiXnd1i0cpes, T(aNnd90wpa, rTmXi9n0dpic, easn)dsu WchSDI) asFTiXg1u0rpe 1an0 dshToNw1s0 pth(ea nsdpaTtXia9l0 dpisatnrdibTuNtio90np o).f tThhee ccoolld iindiices s(hanowd ewdaarmsig inidfiiccaenst) such as dTeXcr1e0aps inagndtr eTnNd 1(909p% (aCnLd) uTnXd9e0rpa lal nthde TmNet9h0opd)s. aTphpel iecdo.ldF oinr dthiceecso oshl odwayefdr eaq useingcnyificant dec(TreXa1s0ipn)g, tthreendde c(l9in9e%w CasL)a buonudt e2r daalyl sthtoe 3mdeatyhso/ddse caapdpeliferdom. FSoarv tahnen acho–oSla dhealyt ofrtehqeuency (TXG1C0pu)n, dthere tdheecMlinMeK waansd aabboouutt 21 ddaayys/ dtoe c3a ddeayusn/ddeerctahdeeI TfrAoman dSaSveann’snsalho–pSeamheelt htood tsh.e GC The reduction in cool days per year was important in the Sahel with a diminution of unadbeoru tth3et oM3M.7Kda ayns/dd aecbaoduet u1n ddearyt/hdeeMcaMdeK utrnedndert etshtea nITdAab aonudt 1 S.8edna’sy ss/lodpecea dmeeuthnodders. The redthuectSioenn’ sins lcoopoela dnadyIsT Apetrr eynedarte wstsa.s Timhepcoorltdanntig ihnt tdhaey sSawheerle wmiothre ai mdpimoritnaunttioovne or ft haebout 3 to 3G.C7 d(aabyosu/dt e1c7addaey su/nydeearr) tthhea nMtMheKr etsrtenofdt theestd aonmda ianb; ofeuwt e1r.8c odladyns/idghetcsadwee ruenodveerr tthee Sen’s sloSpaev annda hIT. ACo tlrdenidg htetsstpse. rTyheea rcaollsdo ndiegchreta dseadyisn wtrerned movoerret hime wpohrotlaendto omvaeirn ,thwei tGh Cth e(about 17 mdayxsim/yuemar)d tehcarena stheea crreossts otfh tehGe Cdo(−m3aidna;y fse/wderc acdoeldu nidgehrtIsT wAearned otvherS tehne’ sSsalvoapnenaanhd. Cold nig−h4tsd apyesr/ dyeecaard ealusnod edreMcrMeaKs)edan dint hterennodrt hoevrnerp atrhte( −w3hdoalyes /ddoemcaadienu, nwdietrhS etnh’es smloapxei,mum dec−r4e.a5sde aaycsr/odses ctahdee GuCnd (e−r3M daMyKs/,daencda−de2 udnaydse/rd IeTcAad aenudn tdheer SITeAn’)s. Asldodpieti aonnadl l−y4, tdhaeycso/lddecade under MMK) and the northern part (−3 days/decade under Sen’s slope, −4.5 days/decade under MMK, and −2 days/decade under ITA). Additionally, the cold spell duration indicator (CSDI) during the study period (1950–2014) was high along the GC (27 days/year) and the northeastern part (23 days/year), with an obvious decreasing trend under all three analysis methods. The highest decrease trend was located in the Water 2021, 13, 3506 17 of 33 Water 2021, 13, x FOR PEER REVIEW 18 of 34 spell duration indicator (CSDI) during the study period (1950–2014) was high along the GC (27 days/year) and the northeastern part (23 days/year), with an obvious decreasing trend under all three analysis methods. The highest decrease trend was located in the nonrtohrethaesatestrenr naraereaa ((−−00.7.7 ddaayy//ddeeccaade undeerrI TITAA, −, −66d dayasy/sd/deceacdaedeu nudnedr eSre nS’esns’lso pselo, apned, and 4 da4ysd/adyesc/addeec audneduenrd Mer MKM)K, f),oflolollwoweded bbyy tthhee coastal bbaanndd( −(−11d daayy//ddeeccaaddeeu nudnedreIrT AIT,A, −4 da−ys4/deacyas/ddee ucandeeur nSdeenr’sS esnlo’spsel,o apne,da n3d d3aydsa/ydse/cdaedcaed uenudnedre Mr MMMKK).) . FigurFei g1u0r.e S1p0a. tSipala tdiaisl tdrisbturitbiuontio onf otfhteh ceoclodld ininddiciceess ((TX10p,, TN1100pp) )a nadndw waramrmin dinicdeisc(eTsX (9T0Xp9, 0TpN, 9T0Np)9o0fpt)h eoft etmhep eteramtuprerature from ftrhoem CthMe ICPM6 IdPa6tadsaetat.s eTth. Te hredre dcrcorsossesse s(+(+) )ininddiiccaatte posiittiivveet rternendds,st,h tehbel abclkacmki nmuisneus s(−es) (n−e)g naetigvaettirveen dtrse, nandds,t hane dgr teheen green circlec siricglne isfiigcnainfitc atrnetntrdesn dwsitwhi t9h59%5% CLC.L . HoHwowevevere,r ,tthhee wwaarrmmd adyasy(sT X(T9X0p9)0apn)d awnadr mwnairgmht sn(iTgNh9ts0 p()T,Nas9w0pel)l,a asst hwe ewlal rams stpheel lwarm spedlul radtiuornatiinodne xi(nWdeSxD I)(,WreSvDeaIl)e, drseovmeaelevder ysohimghe- tevmerpye rahtiugrhe-etevmenptser(awtaurrme eexvtreenmtse s)(.warm The significant trend for the TN90p was well captured over the whole domain with all exttrheemsteast)is. tTichael msiegtnhiofdicsaunste tdr.eHndo wfoerv ethr,eI TTANd9i0sppl awyaesd wa edlyl ncaampticuraendd osmveoro tthhes owuhthowlea rddomain wigthra daliel ntth,ew sitthattihsetipcaeal kmoef tthhoedinsc ruesaesde .n oHtiocwedeavleorn, gITthAe GdCis(pmlaoyreedth aan d+y2ndaamysic/ daencdad sem) ooth souanthdwaalorwd egrriandcrieeanste, iwn itthhe nthoert hpeerankp aorft (tahbeo uintc+r1eadsaey /ndoetciacdeed) .aTlhoengin ctrheea sGinCg t(rmenodrew aths an +2 dasylsig/dhtelcyaedqeu)a lalyndi satr ilbouwteedr aicnrocsrseathse sitnud tyhdeo mnoarinth, ewrinth pabaortu t(+ab1.o5udta y+s1/ ddeacyad/deeucnader). The incSreena’sinslgo ptreetnedst wanads sallimghotsltyd eoquubalellyu sdiinsgtrtihbeutMedM aKcrtoests (t3hed astyusd/dye dcaodmea),inex, cwepitths aobmoeut +1.5 dasypsi/kdeescoavdeer Luankde eCrh aSdeann’sd tshleobpoer dteersjtu nacntidon ablemtwoesetn dBouurkbilnea Fuassionagn dthCeo teMdM’IvKo irete. st (3 daFyosr/dTeXc9a0dpe,)t,h eexmcaegpnti tsuodme oef tshpeiksiegsn iofivcaenr t Ltraeknde sCwhaasdre gainodna ltlhyel obcaotredde, rw ijtuhntchteiohnig hbeertween Bu+r1k.i4ndaa Fyas/sod eacnadd eCuontde edr’SIveno’isres.l oFpoer (T+X3.920dpa, ytsh/ed mecaadgne iutunddeer oMf MthKe )siingnthifeicnaonrtt htreanndds was the lower +0.5 day/decade under Sen’s slope (+1.2 days/decade under MMK) in the regsioountha.llSyi mloilcaartterden, dwsiwthe rtehen ohtiicgehdefro +r1th.4e dWaSyDs/IduescinagdeM uMnKdearn dSeITnA’s tselsotsp,eb u(+t3th.2o udgahysth/deecade untdreenrd sMwMerKe )w einll dtehpei ctneodrbthy aamndin imthuem loofw8e0r% +o0f .t5h edmaoyd/delesc, athdeey wunerdeenro tSseingn’si fiscalonptley (+1.2 da(yast/dleeacsat d9e5% unCdLe)ri nMdMicaKte)d inb ythae msoinuitmh.u Smimofila80r %treonf dthse wmeored enlso.tiTcehdis fnoorn tshige nWifiScaDnIt using MMrepKr easnednt aITtiAon tecasnts,b beucot nthfiormugedh bthyet htreecnodnst rwasetrseh wowelnl wdeitphicthteeds pbayt ia lmdinstirmibumtio nofo 8f0% of theS emn’osdselolsp,e t(hFeigyu wree1r0e) ,nwoht iscihgncliefaicralyndtliysp (laty eledatshte 9c5h%an CgeLs) iinntdwicoattreedn dbsy, ian cmreiansiinmguomve or f 80% of tthhee nmorotdhe(l+s0. .T6hdiasy n/odnecsaigdne)ifaincadndte rcerpearseisnegnotavteirotnh ecasnou bteh c(−on0f.3irdmaeyd/d beyc atdhee) .contrast shown with the spatial distribution of Sen’s slope (Figure 10), which clearly displayed the changes in two trends, increasing over the north (+0.6 day/decade) and decreasing over the south (−0.3 day/decade). 3.3.2. Absolute Extreme Temperature Indices (TXX, TXM, TMM, TNX, TNN, TNM, TXN) Figure 11 presents the spatial average of the maximum values of daily maximum temperature (TXX), mean daily maximum temperature (TXM), mean daily mean temperature (TMM), and maximum value of daily minimum temperature (TNX). As Water 2021, 13, 3506 18 of 33 Water 2021, 13, x FOR PEER REVIEW 3.3.2. Absolute Extreme Temperature Indices (TXX, TXM, TMM, TNX, TNN, TNM, TXN)19 of 34 Figure 11 presents the spatial average of the maximum values of daily maximum temperature (TXX), mean daily maximum temperature (TXM), mean daily mean temper- ature (TMM), and maximum value of daily minimum temperature (TNX). As analyzed anaplryezveiodu pslryevfoirouthselym foaxr itmhue mmatexmimpeurmat uteremtpreenrda,tuthree tmreanxidm, tuhme mTXaXxiwmausmlo TcaXteXd winasth leocated in nthoert hneorrnthpearrnt, paanrdt,s apnecdi fiscpaellcyiftihcaelnlyo rtthhew neostrt(h45w◦eCst). (T4h5e °Csa)m. Tehwee snatmfoer wTXeMnt (f4o3r ◦TCX),M (43 °CT),M TM,Man, danTdN XT.NTXh.e Tlohwe elsotwmeasxti ma xwiemrea lwocearted loincattheeds oinu tthheer nsoauretah.eTrhne aarneal.y Tsihsew aansalysis wabsa bseadseodn otnh ethceri tcerriitoenriothna tthaamt ain miminuim uomf 8 0o%f 8o0f%m oofd melos dhealds thoasdh toow shthoewt rtehned t;rtehnuds;, thus, thet hreedre dcrcorsossesse s(+(+) )ininddiiccaattee tthhaatt aatt leleaastst8 08%0%o fomf omdoedlsealsg raegedrewedit hwtihthe itnhcere ianscinregatsrienngd trend (Fi(gFuigrue r1e11)1. )T. hThe eggrereeenn cciirrcclleess iinnddiiccaatetew whehreerteh tehtere tnrdens dwse rweesrigen siifigcnanifticaatn9t5 %at C9L5%. CL. FigurFei g1u1r. eS1p1a.tSiapla dtiaisltdriibsturitbiounti oonf othf eth me maxaixmimuumm vvaalluuee of the daaiilylym maxaixmimumumte mtepmepraetruarteu(rTeX (XT)X, tXh)e, athver agveroafgthe eodf atihlye daily maximuaxmim tuempt emrapteurraet u(rTeX(MTX)M, t)h, eth aevaevreargaeg eoof fththee daiily meaann tetemmppereartautruere(T (MTM)M, a)n, danthde tmhea xmimauxmimvuamlu evaolfudea iolyf daily minimuinmim tuempteemrapteurraetu (rTeN(TXN).X T).hTeh reerde dcrcorossseess ((++)) iindicate ppoossitiitviveetr ternednsd, sth, ethbela bclkamcki nmusinesu(s−es) n(−e)g anteivgeatrivened tsr,eanndds,t haend the greeng creirecnlecsir csliegsnsiifgicnaifincta tnrtentrdensd ws iwthit h959%5% CCLL. . ReRgeagradridnign gtrternendds,s a, lall tlhteh estsattaitsitsitcicaal lmmeetthhooddss sshhoowweedd aann iinnccrreeaassiinngg ttrreennddi inn mmaaxxi-imum temmpuemthe nra tteumrepse. rTahtuer essig. nTihfiecasingtn iinficcraenatsien cinre masaexiinmmuamxi mtemumpetreamtuperatures was higher inorth and lower in the south, with the appearance of a clearresd wynaasm hiicgnhoerrt hinw tahred north andgr laodwieenrt .inS ethne’s ssoloupthe, twesitthin tdhicea atepdpeaanriannccreea osfe ao cf laebaoru dty0n.1amtoic0 .n3o◦rCth/wdeacradd eg;rtahdeieITnAt. Sen’s slompee ttheosdt ,in0.d1itcoa0te.2d5 a◦nC /indcerceaadsee; aonf dabthoeuMt 0M.1K tom 0et.3h o°dC,/advecearydeh;i gthhe0 .I2TtAo 0m.5e◦tCho/dd,e c0a.1d et.o 0.25 °CT/dheecamdaex;i manudm tohfe TMmMaxKw maseltohcoadte,d ai nvethrye shaimghe a0r.2ea to(n o0r.5th °eCrn/dpeacrat)due.n dTehre thmeatxhirmeeum of Tmmaext hwoads luosceadt,ewd iitnh itnhcer esasmeseo afr0e.a15 (n◦Cor/tdheecrand ep,a0r.t2)5 u◦nCd/edre cthade et,harnede 0m.3e5th◦Cod/sd eucsaedde, with incurneadseersI ToAf ,0S.1en5 ’s°,Ca/nddecMadMeK, ,0r.2es5p °eCct/idveelcya.dTeh, ealnodw e0s.3t 5v a°lCue/doefctahdeem uanxdimeru mITAof, TSmena’xs, and MMwaKs, arcersopssectthieveGlyC.. ITnhceo nlotrwasets,tt hvealIuTAe oofv ethrees tmimaaxtiemd uthme ionfc rTeamsianxg wtreans dacorfotshse tmhaex Gi- C. In conmtruamst,o fthTem IaTxAo voevretrheestCimamateerdo otnhem ionucnretaaisnisngan tdretnhde Goaf btohne fmoraexsitmcoummp aorfe dTmtoaSxe no’vser the CaamnedrMooMn Km. Tohuisntraepinrse seanntadt iotnheo f GITaAbowna s fdoureestto tchoemfapcatrtehda t ittob rSoeung’hst oauntdt heMsMmKal.l This representation of ITA was due to the fact that it brought out the small trend changes or trends over each year. The ratio of the maximum Tmax to the minimum Tmax (TXN) was 2.1 (Figure 12), which means that the maximum of Tmax was about 2.1 times greater than the minimum of Tmax, and this was almost verified under all three of the analysis methods adopted. The spatial average of Tmax, as well as the average of the mean temperature in Figure 12, expressed the same tendency of the increasing value from ITA to Sen’s to the MMK. In the southern regions, increasing trends were around 0.09 °C/decade, 0.07 °C/decade, and 0.2 °C/decade for the mean of Tmax and 0.09 °C/decade, Water 2021, 13, 3506 19 of 33 trend changes or trends over each year. The ratio of the maximum Tmax to the minimum Tmax (TXN) was 2.1 (Figure 12), which means that the maximum of Tmax was about 2.1 times greater than the minimum of Tmax, and this was almost verified under all three of the analysis methods adopted. The spatial average of Tmax, as well as the average Water 2021, 13, x FOR PEER REVIEW of the mean temperature in Figure 12, expressed the same tendency of the incre 2a0s oifn 3g4 value from ITA to Sen’s to the MMK. In the southern regions, increasing trends were around 0.09 ◦C/decade, 0.07 ◦C/decade, and 0.2 ◦C/decade for the mean of Tmax and 00..10 9°C◦C/d/edcaedcaed, ea,n0d. 10.◦2C2 /°dCe/dcaedcaed, ae nfodr0 t.h2e2 m◦Ce/adne ocfa dtheef sopratthiaelm aveearnagoef ttheemsppeartaitaulraev uenradgeer ttheme IpTeAra, tSuerne’us,n adnedr tMheMIKTA m, eStehno’sd,sa, nredspMeMctiKvemlye. tThhoed sp,errecsepnetcatgive eolyf .sTighneifpicearncte nintacgreeaosef tsriegnndifi wcaanst 1in0c0r%ea fsoert raenn advweraasg1e0 m0%eafno rteamn paverearatugreem, geraenatteerm tphearna t7u0r%e, fgorre athteer mthaaxnim70u%mf oorf tthhee mainxiimuum otefmthpeemraitnuimreu (mTNtexm), paenrdat uarbeo(uTtN 5x0)%, a fnodr atbhoeu atv5e0r%agfeo rotfh Temavaexr. aSgoemofe Tamreaaxs. wSoemree sairgenaisfiwcaenrtelys iignncirfiecaasnintlgy winicthre 9a5si%ng CwLi tbhu9t 5w%erCeL nbout tshwoewrenn boyt sah mowinnimbyuma mofi n8i0m%u mof mofo8d0e%ls oofnm wohdieclhs othnew auhtichhortsh eagaruetehdo rfosra tghree eadnafolyrstihse ina nthaley psirsesinentht estpurdeys.e nt study. Fiigurree 1122.. SSpaattiiaall diissttrriibuttiion off tthee aaveerraagee vaalluee off tthee daaiilly miiniimum tteempeerraatturree ((TNM)),, tthee miiniimum off tthee daaiilly mmaaxxiimmuumm tteemmppeerraattuurree ((TTNNNN)),, aanndd tthhee mmiinniimmuumm ooff tthhee ddaaiillyy mmaaxxiimmuumm tteemmppeerraattuurree ((TTXXNN)).. TThhee rreedd ccrroosssseess ((++)) iinnddiiccaattee ppoossiittiivvee ttrreennddss,, tthhee bbllaacckk mmiinnuusseess ((−−)) nneeggaattiivvee ttrreennddss,, aanndd tthhee ggrreeeenn cciirrcclleess ssiiggnniiffiiccaanntt ttrreennddss wwiitthh 9955%% CCLL.. 3.4. Spatial Changes in Precipitation Indices 33..44..11.. PPeerrcceennttiillee--BBaasseedd PPrreecciippiittaattiioonn IInnddiicceess ((RR9955pp,, RR9999pp)) TThhee aannaallyyssiiss oof fF iFgiugruer1e3 1in3 diincadtiecdattehda tththaet Rth95ep Ra9n5dpR a9n9dp vRa9r9iapb ivliatireiasbwileitrieesr egwioerne- raellgyiodniasltlryib duitsetdri,bwutiethd, twheithm athxeim muamximinutmhe inso tuhteh saonudtht haendm tihneim muimniminumth einn othreth n. oTrthhe. Tfihgeu rfeigmuraer kmsaarnksi nacnr einacsrineagstinregn tdreonfdt hoef tpheer cpeenrtcielentoilfet hofe tthweo twinod iincedsi,cews,i twh istcha stcteartitnergisngosf osifg nsiigfincaifniccaen(caet l(eaats tleaats9t5 %at C9L5)%i nCthLe) sionu tthhe. Lseosustthh.a nLe8s0s% thoafnth 8e0m%o doef lsthues emd oinddeilcsa tuesdeda icnhdaincgaeteidn at hcehannogreth ienr tnhpe anrotrothf ethrne pstaurdt yofd tohme satiundwy idthomSeanin’s wsliothp eS;eint ’sse selmopset;h iat tsetheme ns othrtaht tdhied nnoortthh advied ancolte haravceh aan cglee.arH cohwanegvee.r ,Hboowtheivnedr,i cbeost(hR i9n9dpicaensd (RR9995pp a) nredc oRr9d5epd) vreecroyrldoewd vraeirnyf alollwa mraoinufnatlsl (aFmigouurnet1s 3(aF–ighu).re 13a–h). For both indices (R95p and R99p), the ITA and MMK methods faintly depicted an increasing trend in the north of 0.3 mm/decade and less than 0.4 mm/decade, respectively. All three methods attested to the existence of change in the southern area and confirmed the maxima located around the Guinea highlands, the Cameroon mountains, and the Gabon forests for both indices. The maximum of the total annual rainfall from the heavy rain days (R95p) was about 320 mm/year (located around Guinea, Cameroon, and Gabon); the maximum of the total annual rainfall from the very heavy rain days (R99p) was about 110 mm/year and was located at the same area as R95p. The increasing trend in the Water 2021, 13, 3506 20 of 33 south for both indices was quietly important (from 2 to 4 mm/decade) but of scattered significance. The increasing trend in the heavy rain days was significant (at least 95% CL) over countries such as Gabon, Cameroon, the southern part of Nigeria, the northern part Water 2021, 13, x FOR PEER REVIEWo f Benin, Burkina Ghana, and the southern part of Mali. However, for R99p, the m2a1r kofe 3d4 increasing trend was not significant in the south. Figure 1133.. SSppaattiiaalld disistrtirbibuutitoionno foft htehe xetxretrmemelyelwy ewt eptr epcriepciitpatiitoantio(Rn 9(9Rp9)9, pth),e tvheer yvewryet wpreetc piprietcaitpioitnat(iRo9n5 p(R),9t5hpe),a nthneu alntnoutal ptoretacli ppitraetciiopnitoantiowne tond awyset( PdRaCysP T(PORTC),PaTnOdTth),e aindte nthseit yinotfenthseityav oefr atghee parveecriapgitea tpiorencoipnitwateitodn aoyns (wSDetI Id).aTyhs e(SreDdIIc)r. oTshsees r(e+d) icnrdosicsaetse (p+)o isnitdiviceattree pnodssi,titvhee tbrleancdksm, tihneu bsleasc(k− m) inneugsaetsiv (e−)t rneengdast,ivaen tdrethnedsg,r aenedn tchirec lgerseseing nciirficcleasn tsitgrennifdicsawnti tthre9n5d%s wCLit.h 95% CL. 3.4.2. Absolute Extreme Precipitation Indices (RX1day, RX5day, RX7day) FTohre bsoptaht iainlddiicsepsl a(Ry9o5fpt haenRd XR19d9apy),, RthXe5 dITaAy, aanndd RMXM7dKa ymveathrioedds wfaiidnetllyy adnedpidctyenda man- iincaclrleyasdiencgr etraesnedd ifnr othme tnhoertsho ouft h0.3to mwmar/ddetchaedneo arnthd. leFsrso tmhatnh 0e.4d matma a/dneaclyadsies,, rtehsepeacvteivraeglye. Amlal xtihmreuem mRetXh1oddasy atotfes6t5edm tmo twhea sexlioscteantecde obfe tcwhaenengel ainti ttuhde essouofth5e◦rnN aarenad a1n2d◦ cNo;ntfhiramt eodf tRhXe5 mdaayx,iomfaa bloocuatt1e2d1 amromu,nadn dthteh aGtuoifnReaX 7hdigayh,laonf dasb,o tuhte1 6C9a–m18e7romonm ,mwoeurnetlaoicnast,e adnidn tthee Gaubinoena fhoirgehstlsa nfodrs ,bCoathm ienrdoiocnes, .a TnhdeG mabaoxinm. um of the total annual rainfall from the heavy rain dTahyes i(nRd9i5cpe)s whasd aabopuots 3it2i0v emtmre/nydeaar c(rlocsasttehde awrohuonlde GofuWineAa,, aCnadmtehroeoenx,t arenmd aGaobf othn)e; tchea nmgaexsicmounmve orgf etdhew toitthalt haenneuxtarle rmaianofafltlh feroimnd tihces v. eTrhye hsetarivcyt crraiitne rdiayws e(lRl 9c9ap)t uwreads asboomuet 1sc1a0t tmermed/yseiganr iafincda nwt ainsc lroecaastiendg atrte tnhdes s(a5m%eo afrtehae aws hRo9l5epd. oTmhaei inn)c. rFeiagsuirneg1 t4reindi cina ttehsef osrouththe fthore ebointhd iciensdaicreis ewofa1s tqou1i.e7tlmy mim/dpeocratdaen,t 1(tforo2m.5 m2 mto/ d4e camdme,/adnedca1d.5e)t ob3u.t7 mofm s/cdatetceardede suingdneifricManMceK. T, hSe nin’scrselaospien,ga tnredndIT iAn, thr es hpeacvtiyv erlayi,n odvaeyrs twheasS saivgannifnicaahntt o(awt aleradsts o95u%th .CLIt) wovaesr ocbosuenrtvreieds tshuactht haes tGreanbdons ,o Cf athmeesreoionnd,i tchees wsoeurtehqeruni eptlayrtc ofn Nsisigt enritab, uthten noot rstihgenrinfi cpaanrt oevf eBreynwinh,e Breu.rkTihnea mGohraentah, eandu mthbee sroouftchuemrnu platritv oefd Mayasli.i nHcroewaseevde,r,t hfoerc Rle9a9rply, tshoeu mthawrkaerd itnhcerpeaosintigv etrternendd wwaas snorti esnigtnedif.icIannatd ind ithioen stoouthe. analysis of the R99p and R95p, this may impact the cumulative wet days in the southern region. 3.4.2. Absolute Extreme Precipitation Indices (RX1day, RX5day, RX7day) The spatial display of the RX1day, RX5day, and RX7day varied widely and dynamically decreased from the south toward the north. From the data analysis, the average maximum RX1day of 65 mm was located between latitudes of 5° N and 12° N; that of RX5day, of about 121 mm, and that of RX7day, of about 169–187 mm, were located in the Guinea highlands, Cameroon, and Gabon. The indices had a positive trend across the whole of WA, and the extrema of the changes converged with the extrema of the indices. The strict criteria well captured some Water 2021, 13, x FOR PEER REVIEW 22 of 34 scattered significant increasing trends (5% of the whole domain). Figure 14 indicates for the three indices a rise of 1 to 1.7 mm/decade, 1 to 2.5 mm/decade, and 1.5 to 3.7 mm/decade under MMK, Sen’s slope, and ITA, respectively, over the Savannah toward south. It was observed that the trends of these indices were quietly consistent but not Water 2021, 13, 3506 significant everywhere. The more the number of cumulative days increased, the c2l1eoafr3ly3 southward the positive trend was oriented. In addition to the analysis of the R99p and R95p, this may impact the cumulative wet days in the southern region. Fiigurre 14.. Spattiiall diisttrriibuttiion off tthrree absollutte exttrreme prreciipiittattiion iindiices ((RX1day,, RX5day,, RX77daay)).. The rred crrosses ((++)) iinnddiiccaatteess ppoossiittiivvee ttrreennddss,, tthhee bbllaacckk miinnuusseess ((−)) nneeggaattiivvee ttrreennddss,, aanndd tthhee ggrreeeenn cciirrcclleess ssiiggnniiffiiccaanntt ttrreennddss wiitthh 9955% CCLL.. 33..44..33.. Thrreesshoolld aand Durraattiioon Exxttrreemee Prreecciipiittaattiioon IIndiicceess ((R1100mm,, R2200mm,, R3300mm,, CCDDDD,, CCWWDD)) FFiigguurree 1155 iilllluussttrraatteess tthhee ssppaattiiaallllyy aavveerraaggeedd nnuummbbeerrss ooff hheeaavvyy rraaiinn ddaayyss ((RR1100mmmm)),, vveerryy hheeaavvyy rraaiinn ddaayyss ((RR2200mmmm)),, eexxttrreemmeellyy hheeaavvyy rraaiinn ddaayyss ((RR3300mmmm)),, ccoonnsseeccuuttiivvee ddrryy ddaayyss ((CCDDDD)),, aanndd ccoonnsseeccuuttiivvee wweett ddaayyss ((CCWWDD)).. TThheeyy eexxhhiibbiitteedd aa sslliigghhtt,, iinnccrreeaassiinngg ssoouutthhwwaarrdd ttrreenndd,, aassi ninth tehcea sceaosef Ro9f9 pR9an9pd Ra9n5dp .RT9h5epC. DThDew CaDs vDe rwy ahsig vheirnyt hheignho ritnh (t2h9e8 dnaoyrtsh/ y(e2a9r8) danadysl/oyweairn) athneds loouwth in(6 1thdea syosu/tyhe a(r6)1. TdhaeysC/yWeaDr)w. Tahs eh iCghWiDn twheass ohuigthh (i1n1 2thdea ysos/uythe a(r11in2 dthaeysG/yueinare ainH tihgeh Glaunidnseaa nHdigChalmanedrso oann–dG Caabmoneraonodn–4G8–a6b2ond aaynsd/ 4y8e–a6r2i ndaoyths/eyreaarre ians )oathnedr alorweasin) athned nloowrt hin( 1t2hed anyosr/thy e(1a2r) d. Rayesg/ayredairn)g. Rthegeacrhdainngge thtree nchdasn, Sgeen t’rsesnldosp,e Saennd’s MsloMpKe adnidd MnoMt cKa pdtiudr enositg cnaipfitcuarnet stirgennidfiscabnetc atruesnedosf btheceahuisgeh oafn tdhes thriigcthc arnitder siatraicdt ocpritteedriian atdhoispstetudd iyn (tthhise smonly atu indiym (uthm of 80% of models that needed to express the trend). Focusing on the CDD,single de emcrienaimsinugmt roefn 8d0%w aosf nmootidceedls, tihnatth neeseoduetdh etoa setxopfrNesisg tehr,ea tnrednadn).i Fnocrceuassiengw oans tohbes eCrvDeDd, ionntlhye as osuintghlwe edsetcorfeathseinCgo tnregnod. Fwoarst hneottwiceoda,n ianl ythsies smouetthhoedasst( oSef nN’sigaenrd, aMndM aKn) ianpcprleiaesdet wo aCsD oDbs, egrlvoebdal ilny, tthhee ssotuudthywdeosmt oafi ntheex pCeorniegnoc.e Fdoar tnhoen tswigon aifincaalynstids emcreetahsoindgs (tSreennd’s, aexncde pMt MforKi)n atphpelGieudi ntoea ChDigDh,l agnldobs,awllyh,e trheea nstiundcyre adsoemuapinto e1xpdearyi/endceecda dae nwoanssoigbnsiefrivcaendt, danecdreaassicnagtt etrreonfds,o mexeceinpct refoasr ining ttrheen dGsuoivneera thheignholarnthdesa, stwerhneraen dann oinrtchrweaesset eurnp ptaor ts1 dasayw/delelcaasdceo wuanst roiebssesruvcehda, sanBde nai ns,caTtotegro ,oGf shoamnae, ianncrdeaCsointeg dtr’Ievnodisre o.vTehr ethaen anloyrstihseoafstIeTrAn arenvde anleodrthawcleesatrerann dpadrytsn aams iwc delils tarisb ucotiuonntroifestr esnudchs, wasi tBheanninin, cTroegasoe, iGnhthaneaC, DanDdi nCtohtee dno’Irvtohierren. Trehgei oannsa(luypsisto o1f .I3TdAa yresv/edaelceadd ae) cdleimari nanisdh idnygntaomwiacr dditshterisbouuttiohnt oo0f .t2rednadyss,/ wdeitcha daen. Tinhcereaasssee sisnm thene tCoDf tDh einC tWheD nroervtheaelrend r,eingdioinresc (tulyp, atoc o1n.3t rdaastyws/ditehctahdeeC) dDiDm;inthisehlioncga ttioownsarodf tChDe Dsomutahx tiom 0a.2c odianycsid/deedcawdieth. Tthhee amssinesimsmaeonft CoWf thDe aCnWd Dvi creevveearlsead., TinhdeiMrecMtlKy, aan cdonSterna’sst wsloitphe tthees tCsDdeDp;i tchteed l,oocvateirotnhse oGf uCiDneDa mhiagxhilmanad csoainncdidCeadm weriotho nth, ea mdeicnriemasai nogf CtrWenDd ainndC WvicDe. Avenrsian.c rTeahsei nMg tMreKn dawndas nSeonti’cse dsloovpeer tthesetsS advaenpnicathed, a, nodvienr ththeec aGseuoinfetah ehMigMhlKantdesst ,atnhde Cchaamnegreoionns, oam edpecarretasswinags ctarepntudr eidn bCasWedDo. nAtnh eianncraelyassiins gcr ittreerniad. was noticed over the The R10mm, R20mm, and R30mm showed increasing trends over the whole of WA, with values of 0.12 to 0.2 day/decade, 0.04 to 0.1 day/decade, and 0.04 to 0.06 day/decade, respectively, over the Savannah and the south area under the ITA test. The Sen’s slope and MMK tests displayed the same trend dynamic, i.e., a southward increase in the number of days per decade. The increasing trend of the R10mm was significant in the southern regions, at about 1 day/decade under Sen’s slope and 2 days/decade under MMK. There was almost no trend of R10mm over the northern regions. The R20mm and the R30mm increased from the Savannah to the south. MMK and ITA, based on our criteria, captured the change, while Sen’s test could not. However, the Sen’s slope results depicted on average Water 2021, 13, 3506 22 of 33 Water 2021, 13, x FOR PEER REVIEW 23 of 34 an augmentation of about 0.2 to 0.4 day/decade in the very heavy rain days in the southern part. In the case of R30mm, the observations were quite similar to those for the R20mm when applying Sen’s slope test. It was also noticed, based on the analysis criteria, that only Satvhaensnouathh,e arnndp airnt othf eth ceaWseA oefx tpheer iMenMcedKa tensotn, stihgen icfihcaanntgien cinre assoemtree npda,rwtsi twh apsic ckaspatruourendd based ont hthe eG auninaelayshiisg hcrlaitnedrsia. . FiguFreig 1u5re. S15p.aStipaal tdiailsdtriisbtruibtiuotnio onfo tfhteh ennuummbbeerrss ooff hheeaavvyy rraainind dayasys(R (1R01m0mm)m, v)e, rvyehryea hveyarvayin rdaainy sd(aRy2s0 m(Rm2)0,mexmtr)e,m eexltyremely heavhye araviynr daianydsa (yRs3(0Rm30mm)m, c),ocnosnesceuctuivtiev eddrryy ddaayyss ((CCDDDD)),, aannddc ocnosnesceuctuivteivwe ewt deta ydsa(yCsW (CDW). TDh)e. Trehdec rroedss ecsro(+ss)eins d(i+c)a tiendicate positpivosei ttirveentdresn, dths,et hbelabclka cmk imniunsuesse s(−()− n)engeagtaitvivee ttrreennddss,, aanndd tthheeg grereenenc icrcirleclsei gsnigifinciafnictatnretn tdresnwdisth w9i5t%h 9C5L%. CL. The R10mm, R20mm, and R30mm showed increasing trends over the whole of WA, with values of 0.12 to 0.2 day/decade, 0.04 to 0.1 day/decade, and 0.04 to 0.06 day/decade, respectively, over the Savannah and the south area under the ITA test. The Sen’s slope and MMK tests displayed the same trend dynamic, i.e., a southward increase in the number of days per decade. The increasing trend of the R10mm was significant in the southern regions, at about 1 day/decade under Sen’s slope and 2 days/decade under MMK. There was almost no trend of R10mm over the northern regions. The R20mm and the R30mm increased from the Savannah to the south. MMK and ITA, based on our criteria, captured the change, while Sen’s test could not. However, the Sen’s slope results Water 2021, 13, 3506 23 of 33 3.4.4. Other Indices (PRCPTOT and SDII) The annual average of PRCPTOT (Figure 13) had a similar spatial distribution and trend to those of R95p and R99p, wherein the values of indices were higher in the southern regions (2000 mm/year around Guinea, Cameroon, and Gabon). Its positive southward trend was about 2 mm/decade (from Savannah to GC) for all the statistical tests analyzed. As the case of previous indices, ITA revealed a very slight trend in the northern area, as did Sen’s slope and MMK, of about 0.02 mm/decade. The spatial distribution of the simple precipitation intensity indicator (SDII) presented in Figure 13, indicates a faithful correlation with PRCPTOT, as well as with R99p and R95p. The maxima and minima were located in corresponding areas from one index to another. SDII reached a maximum and minimum of 10 mm/day/year and less than 1 mm/day, respectively. According to the statistical methods and criteria applied in the present study, Sen’s slope and MMK indicated a high positive trend in the SDII over the Savannah of about 2 mm/day/decade and a lower positive trend in the northeast and southwest of Cote d’Ivoire of about 0.01 mm/day/decade (almost no trend). ITA also displayed a positive trend in the SDII, but in contrast with the results obtained under MMK and Sen’s slope, the maximum of the increase lay in the northern regions, with an increase up to 0.5 mm/day/decade. The trend in the central part of WA rose to 0.25 mm/day/decade. 3.5. Temporal Variability of Precipitation and Temperatures Indices In this part, for better evaluation of the temporal variabilities [91], temporal analysis of trends was performed over five subregions [92] (West Sahel: WSHL, Central Sahel: CSHL, East Sahel: ESHL, West Guinea Coast: WGC, and East Guinea Coast: EGC) and the whole study domain (West Africa: WA).The cold indices TX10p, TN10p (Figure 16a,b), and CSDI (Figure 17k) globally expressed a temporal negative trend. The change trends were almost identical for the five subregions and the whole domain. A slight upward trend of about +0.05 day/decade was revealed from 1950 to 1962, when the largest value is recorded, followed by a consistent detrend of about −0.35 day/decade from 1963 to 2014, with a spike (break) in 1992. However, the warm indices TX90p and TN90p (Figure 16c,d), as well as WSDI (Figure 17l), displayed two major trends, a slight upward trend (0.01 day/decade) in 1950–1992 (1950–2000) for TX90p and TN90p (WSDI), and the positive trend became more important (0.46 day/decade) in 1993–2013 (2001–2014) for TX90p and TN90p (WSDI), up to an average maximum value of 16 days and 19 days (16 days) a year, respectively. For WSDI, the ESHL increased greatly from 2010 to 2012 and seemed to exhibit the same increase as in the case of TX90p, while over WGC, the TN90p, which recorded smaller values since 1950, rose up starting in 2000 to reach its peak in 2013. The above analysis indicated that nighttime cooling was higher than daytime cooling in the period 1993–2014, and over the same period, it was observed that nighttime warming was higher than daytime warming. Figure 17 depicts the overall upward trend for indices including RX1day, RX5day, RX7day, R10mm, R20mm, and R30mm, as well as R99p, R95p, and PRCPTOT. All the indices showed both upward and downward trends (not clear trends) from 1950–1992. However, from 1993–2014, they slightly increased. For these indices, the WGC received the maximum rainfall amount, followed by the EGC and the whole domain. Very large differences between values were noted from one subregion to another. Obviously, the values of WSHL and CSHL were close, since they are located at the same latitude. This confirms that the rainfall experience over West Africa was due to the swaying of the West African Monsoon (WAM). The temporal representation of the change in CDD is opposite to that of the change in CWD for each subregion. There was an increase in CWD over WSHL, CSHL, and ESHL and a slight decrease over WGC and EGC, but the trends were mixed over the whole period. The overall trend (WA) was upward, with +0.012 day/decade. For the subregions, there was no obvious trend in CDD. However, the highest value of CDD was registered over the ESHL and seemed to then decrease from 1992–2014 by 0.009 day/decade. The CSHL trends were alternatively upward and Water 2021, 13, 3506 24 of 33 downward in 1950–2014, while the upward and download trends over WSHL led to an overall increase of about +0.024 day/decade. The lowest values of CDD were located in WGC, which showed a decreasing trend in CDD over the whole region. On the other side, Water 2021, 13, x FOR PEER REVIEW the lowest CWD was closed, located over WSHL, CSHL, and ESHL, and overall followed25 of 34 an upward trend of about 0.021 day/decade. The highest CWD (about 59 ± 6) was located in subregions such as WGC and EGC but showed a decreasing trend over time. FigurFei g1u6.r eT1e6m. pTeomraplo vralrivaabrialibtielisti eosf othf teh 1e01 0abasbosolulutete iinnddiices ((TaN) T1N0p10, pT,X(b1)0pTX, T10Np9, 0(cp),T TNX9900pp, ,( dT)XTX,9 T0pX,M(e,) TMXXM, (,f )TTNXXM, ,TNN, TXN)( ga)nTdM thMe, c(ho)nTseNcXu,ti(vi)eT dNrNy ,d(ja)yTsX (NC)DaDnd) athnedc coonnsesceuctuivtievder wy deat ydsa(y(ks )(CDWDD) )a.n d consecutive wet days ((l) CWD). Figure 16 presents the absolute indices, i.e., maximum of Tmax (TXX), mean of Tmax (TXAM s)l,igt het auvpewraagredo ftrtehnedm oeaf nabteomupt e+r0a.t0u5re d(aTyM/dMe)c,atdhe mwaaxsi mreuvmeaolfedTm frionm(T N19X5)0, tthoe 1962, whmenin itmheu mlarogf eTsmt avxal(uTeX Nis) , raencdortdhedm, infoimllouwmeodf Tbmy ian (cToNnNsis),teantd dinedtriceantdes oaf paobsiotiuvte −0.35 daytr/ednedcafdore efarcohmo 1f 9th6e3m too 2v0e1r4t,h ew6it5hy ae asrpsik(1e9 5(b0–re20a1k4)) .inT 1h9e9c2h. aHngoewienvtehre, tchitee dwianrdmic eins dices TXw90aps oavnedra lTl Npa9r0apll e(lFfirgoumrea r1e6gci,odn),t oasa nwotehlel ra, sa nWd SaDbrIe a(Fkiginutrhee 1tr7eln),d diinsp19la9y2ecdo utlwdob emajor trennodtse,d af oslrigalhl ◦ to uf tphwemar.dT XtrXenrods e(0b.0y10 .◦d 0a2y/Cd/edcaecdaed)e infr o1m95109–5109–9129 9(21,9a5n0d–2fr0o0m0) 1f9o9r3 T–2X09104p, and TNt9h0epu p(WwaSrDdIc)h, ◦a anngde twhea spaobsoituivt 0e. 0tr7enCd/ bdeeccaamdee. TmXoMre, TimXNpo, artnadnTt N(0X.4p6o dsaityiv/edleyccahdaen)g iend 1993– by about 0.01 C/decade from 1950–1992 but from 1993–2014 increased at rates of about 20103.0 (62◦0C0/1–d2e0ca1d4e) ,f0o.0r 1T◦XC9/0dpe caandde, TanNd900.p05 (9W◦CSD/dI)e,c audpe ,tore sapne catviveerlayg. e maximum value of 16 days and 19 days (16 days) a year, respectively. For WSDI, the ESHL increased greatly from 2010 to 2012 and seemed to exhibit the same increase as in the case of TX90p, while over WGC, the TN90p, which recorded smaller values since 1950, rose up starting in 2000 to reach its peak in 2013. The above analysis indicated that nighttime cooling was higher than daytime cooling in the period 1993–2014, and over the same period, it was observed that nighttime warming was higher than daytime warming. Water 2W0a2t1er, 21032, 1x, 1F3O, 3R5 0P6EER REVIEW 25 of 3326 of 34 FigurFei g1u7r.e 1T7e.mTepmorpaolr avlavrairaibaibliltiiteiess of tthheei nidnidceicse(sa )RRX1day,, (bR)XR5Xd5adya,y ,R(Xc)7RdXa7yd, aRy,1(0dm) Rm1,0 mRm20,m(em) R, 2R0m30mm, m(f), RR3909mpm, ,R95p, PRCP(gT)ORT9,9 pSD, (hII), RC9S5Dp,I,( ia)nPdR CWPSTDOIT., (j) SDII, (k) CSDI, and (l) WSDI. 4. Discussion FigSuomree 1r7e cdenept sictutsd itehse[ 4o6v,4e7r,a9l3l ]uinpvwesatridga ttreedntdh efopre rifnodrmiceans ciencolfuCdMinIgP 6RmXo1ddealys,a RndX5day, RXf7oduanyd, tRha1t0tmhemy ,h Rad20ambemtt,e ranpedr fRor3m0manmce, iansr ewgealrld atso pRr9e9cpip, itRa9ti5opn, thanand CPMRICPP5TmOoTd.e lAs.ll the indIniceths eshporewseendt rbeosteha ruchp,wthaerds paantida ldaonwdntewmaprdor atrleenvdoslu (tnioont oclfeeaxrt rtermenedcsl)i mfraotme e 1v9en50ts–1992. Hoowvervtehre, fwrohmole 1o9f9W3–e2s0t1A4f,r itchaeayn sdliigtshtfilvye isnucbrereagsieodn.s Fdourr itnhgesthe einpderiicoeds,1 t9h5e0 –W20G14Cw raesceived thea nmaalyxziemdubmas erdaionnfatlhle aCmMoIuPn6td, afotallsoewt beyds eblye ctthineg EsGomCe aindi ctehseo wf ehxotrlem deotmemaipne.r Vateurrye large difafenrdepnrceecsi pbiteattwioene. n values were noted from one subregion to another. Obviously, the values Tohf eWpSreHciLp iatantdio nCSoHveLr wWeArew calsoswe,i dseinlyced itshtreiyb uatreed lwochaetnedc oant stihdeer sinagmien dlaivtiitduudael. This conmfiordmelss .thAast atfhoer ermaiennftaiolln eexdp, emroiennscoeo novpeerri Wodersati nAcforinctar iwbuatse sdmueo re t◦ ◦ to t hhaen s8w5%ayoifntgh eoft otthael West Afrainnufocuann al precipitati d MthoatnisnoSoenp t(eW onAovMe)r. tThehela tteitmudpeosr5al Nre–p2r0esNen. tTahtiiosnse oefm tshteo cahgarneegew iinth [94], wmber, the rainfall was the highest (about 30.85% contributio nCoDf Dth eis h t oo ich tpalposite to athnnaut aolfa mthoeu cnht)aanngdes liing hCtlWy iDnc rfeoars eedafcrho msuAburgeugsiot (n2.9 .T4h4%er)e. Twheasp raense nintcsrtueadsyei liluns tCraWteDd over WSthHaLt ,t hCeSaHreLa, awnitdh EaSdHeLcr aeansde ain sltiogthalt adnencureaal srea ionvfaelrl, WasGrCev aeanlded EbGyCt,h beumt tohdee ltsr,ewndass were mitxheedG uoivneera Hthigeh lwanhdosl,ew hpieleriCodam. eTrohoen ,oGvaebroanll, atnredntdhe (SWavAan) nawhaesx huibpiwtedartdh,e hwigithhe st+0.012 dayin/cdreacsaidneg.t Freonrd tsh(eF isguubrree5g).ioRnesg,a trhdeinreg wthaese xntore ombevpioreucsip tirteantidon inin CWDAD, .t hHeoswtuedvyedre, pthicete hdighest valsutreo nogf ClinDkDs o wf iatss crheagnisgteesretodc olivmeart itchzeo EnSesHaLn danshdo sweeedmietsdi ntocr tehaesen odveecrrtehaesset ufrdoymp e1r9io9d2–2014 by 0.009 day/decade. The CSHL trends were alternatively upward and downward in 1950–2014, while the upward and download trends over WSHL led to an overall increase of about +0.024 day/decade. The lowest values of CDD were located in WGC, which showed a decreasing trend in CDD over the whole region. On the other side, the lowest CWD was closed, located over WSHL, CSHL, and ESHL, and overall followed an upward Water 2021, 13, 3506 26 of 33 in Savannah. The identification and location of the trends were the same under all the statistical methodologies applied; the trends differed only in their magnitudes. The trends in precipitation were enhanced with the results on percentile-based metrics, which revealed the existence of a gradual north–south trend. The result converged with [95], which noted a drying trend over WA from 1951 to 2012. Additionally, ref. [96] noticed that during the period 1990–2010, both annual rainfall and the frequency of rainy days increased, leading to partial recovery from the severe dry period recorded in WA in the 1970s. This dryness was studied in [97,98], wherein it was illustrated that at the beginning of the 1970s, all climatic zones in tropical West Africa, from the arid Sahelian to the humid Guinea Coast climate, experienced a decade-long period of below-normal annual rainfall amounts. This finding converged with analysis of the temporal variability of trend, which revealed the existence of a breakpoint in 1992. Ref. [97] noted a decrease in the number of rainy events over the central Sahelian country of Niger in the two dry decades from 1970 to 1989; the results showed a quasinormal condition (or a slight increase) for temperature and precipitation from 1950–1992. A study led by [99] associated the “recovery” of WA in the 1990s to greenhouse gas (GHG) changes noticed between 1910 and 2008. This recovery in rainfall will influence the climate water balance (CWB, refs. [20,24]) and increase uncertainty in regard to hydrology, agriculture, and climate change. Although a general downward trend was noticed in the north, the ITA and MMK tests captured the existence of slight increases in R95p and R99p within the region and confirmed the maximum location as well as an upward trend in the amount of rainfall received around the Guinea highlands, Cameroon mounts, and Gabon forests. These results agreed with previous studies [47,100], which identified similar trends. Additionally, the present study indicated that the more the number of cumulative days increased, the more important the positive trend in the amount of rainfall received in the south was. R95p illustrated a significant positive trend in heavy rainfall near countries such as Gabon, Cameroon, the southern part of Nigeria, the northern part of Benin, Burkina Ghana, and the southern part of Mali. According to [101], it was expected that the number of consecutive dry days would be more pronounced in the northern regions than in the southern. The same analysis led to the conclusion in [102] that rainfall in the littoral zone of southern WA was more extreme than that inland. The temperature has, over most regions across the world, an increasing trend. The annual means of daily maximum and minimum temperature exhibited a significant increase during the focus study period (1950–2014). Ref. [103] discovered the same warming trends over WA, while [104,105] found similar results in Eastern and Southern Africa, respectively. Ref. [106] reported that the temperature increase in high latitudes was greater than that in low latitudes. Regarding the temperature extreme changes in the whole of WA, TN90, TX90p, and WSDI indicated a general warming trend. That result was in line with the findings in [107] during the period 1979–2005, where an upward trend was noted in TX90p and TN90p associated with an increase in WDSI. The cold nights (days), TN10p (TX10p), showed a southward declining trend. This observation was confirmed by [101], which identified uniform declining trends in TN10p and TX10p over WA. Furthermore, during the period 1950–2014, the trends of cool/warm nights (TN10p/TX90p) were more significant than those of cool/warm days (TX10p/TX90p); this justifies the finding of [56], which reached the same conclusion over China. The absolute extreme temperature indices, such as TXX, TXM, TMM, TNX, TNN, TNM, and TXN, experienced an increasing trend over the whole study area. The Sahel area became warmer, with significantly high values noticed; a clear northward dynamic gradient was well represented. Overall, clear warming weather events were experienced in WA, with significant increases in all the extreme temperatures. This change in climate conditions revealed the manifest effect of climate change in the study domain. Studying the temporal variability and trends of temperature, the study revealed that the cold indices TX10p, TN10p (Figure 16a,b), and CSDI (Figure 17k) expressed a general temporal negative trend. Those indices had breakpoints within their trends in 1962 in terms of temperature change because, prior to the clear declining trend, the indices Water 2021, 13, 3506 27 of 33 observed a slight increase from 1950 to 1962 before adopting an upward trend until the end of the study period (2014). The present study revealed that in WA, very few positive trends of CDD were generally observed, with the maxima converging in the northern part as well as over some other countries (Benin, Togo, Ghana, and Cote d’Ivoire). However, the increasing trends of CDD reduced southwardly. A contrast was noticed in that the area receiving a lot of rainfall (Guinea Highlands, Cameroon mounts, and Gabon forests) displayed a decreasing trend in CWD, while the savannah was more wet. The trends regarding CDD and CWD confirmed findings from [103]. The negative trend in CWD was directly correlated with a southward increase of R10mm, R20mm, and R30mm in WA, similarly as in the findings in [20], which detected increasing R10mm and R20mm over the orographic regions and the ocean boundary (Gulf of Guinea). Changes in CDD and CWD can lead to uneven temporal distributions of rainfall. It is manifest that the persistence in the upward trend in CDD and the simultaneous downward trend in CWD led to a negative impact on the PRCPTOT trend over the study domain and could have a negative influence on the water resource demand in WA in general and especially in the northern regions. This may lead to efforts to further irrigation plans to supply the needs of water for agriculture production in the northern area. Furthermore, the southward diminution in CWD might induce a reduction in the number of times WA is watered, and the concurrent upward noticed in SDII and the R30mm might induce localized floodlike situations over the southern regions. It is important to note that CDD and CWD are crucial in the magnitude of flooding (especially flash flooding) events because of their implications on the soil moisture state before the occurrence of flooding. Moreover, as [108] indicated, increasing soil moisture reduces the infiltration capacity of the study domain and then fosters flooding occurrence. A very strong correlation was noticed among R99p, R95p, and PRCPTOT. The increase in PRCPTOT over WA was illustrated in previous studies [103,107]. This attests that other rainfall events over the area did not contribute much to the total annual rainfall amount. Indirectly, extreme rainfall events increased in intensity in the southern area and might be the most accountable for the total annual rainfall. 5. Conclusions In this study, the long-term spatial and temporal variabilities of and changes in rainfall and temperature were analyzed. Based on climate indices suggested by ETCCDMI, the study applied three statistical tests to assess the trends in the two variables. The following conclusions from the study can be made: 1. The total annual rainfall was found to decrease around the coastal area, especially over the Guinea highlands, the Cameroon mountains, as the Gabon forests, but increase over the Savannah and Sahel regions. Furthermore, rainfall during the monsoon months contributed more than 85% of the total annual rainfall in the study domain. 2. The interannual Tmax and Tmin both followed the same trends as the total annual rainfall, with a northward gradient. The warmest region was the Savannah–Sahel, while the coastal part was the coolest area. Using ITA, particular increasing trends were identified from the models in the northern part and the Guinea coast. The increasing trends around the Guinea coast may be due to the gradual increase in sea surface temperature (SST) due to global warming (GW). 3. Extreme high-temperature indices (warm extremes) significantly increased, while the cold extremes indicated a significant upward trend. Both indices showed a breakpoint (abrupt changing point) in 1992, after which the trend increased more in power. 4. The study revealed that the more the number of cumulative days increases, the more important the positive trend in the amount of rainfall received in the south was. Based on analysis of indices such as TN90, TX90p, and WSDI, the study also indicated a general warming trend over the whole of WA. However, over the study period, the trends in cool/warm nights (TN10p/TX90p) are more significant than those in cool/warm days (TX10p/TX90p). Water 2021, 13, 3506 28 of 33 5. The upward trend in CDD and simultaneous downward trend in CWD led to a negative impact on the PRCPTOT trend over WA. This may affect water resource demand in general in WA and especially in the northern areas. Moreover, the decline in CWD showed a reduction in the wet spells in WA, and the concurrent upward notice in SDII and the R30mm might induce localized floodlike situations over the southern regions. Thus, it is important to note that CDD and CWD are crucial in regard to the magnitude of flooding events because of their implications on the soil moisture state before the occurrence of floods. 6. The innovative trend analysis (ITA) methodology applied in this work was able to capture the most minute trends existing in a time series, including some that could not be detected by the usual tests so far used, such as Mann–Kendall and Sen’s slope. The reliability of ITA in tracking unseen trends in time-series data encourages us to recommend it to the reader as a reliable method to be used in time-series trend detection. 7. Information gathered together from this study can contribute to producing sustainable water resource planning and management. It could also be useful for policy makers and scientists for exploring extreme climate event trends on regional and local scales to plan the circumstances in which potential floods and droughts might occur. Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/w13243506/s1, Figure S1: Spatial distribution of the contribution of the West African Monsoon in the annual rainfall over the period 1981–2014 based on the observed data (CHIRPS and CRU) and the selected CMIP6 dataset. Figure S2: Spatial distribution of the minimum temperature (Tmin) over the period 1981–2014 based on the observed data (CHIRPS and CRU) and the selected CMIP6 dataset. Figure S3: Spatial distribution of the minimum temperature trend over the period 1950–2014 based on the CMIP6 dataset and Sen’s slope test. Figure S4: Spatial distribution of the minimum temperature trend over the period 1950–2014 based on the CMIP6 dataset and the modified Mann– Kendall (MMK) test. Figure S5: Spatial distribution of the maximum temperature trend over the period 1950–2014 based on the CMIP6 dataset and the innovative trend analysis (ITA) test. Figure S6: Spatial distribution of the minimum temperature trend over the period 1950–2014 based on the CMIP6 dataset and the innovative trend analysis (ITA) test. Author Contributions: Conceptualization, G.M.L.D.Q. and N.A.B.K., methodology, G.M.L.D.Q.; software, G.M.L.D.Q.; validation: G.M.L.D.Q., F.N., N.A.B.K. and M.B.S., formal analysis, G.M.L.D.Q.; investigation, G.M.L.D.Q.; resources, G.M.L.D.Q.; writing—original draft preparation, G.M.L.D.Q.; writing—review and editing, G.M.L.D.Q., F.N., N.A.B.K. and M.B.S.; visualization G.M.L.D.Q. and N.A.B.K.; supervision, N.A.B.K. and M.B.S.; project administration, N.A.B.K.; funding acquisition, N.A.B.K. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by a grant from the Government of Canada provided through Global Affairs Canada, www.international.gc.ca (accessed on 1 November 2021), and the International Development Research Center, www.idrc.ca (accessed on 1 November 2021). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: CMIP6: https://esgf-data.dkrz.de/search/cmip6-dkrz/ (accessed on 18 June 2021). CHIRPS: https://data.chc.ucsb.edu/products/CHIRPS-2.0/africa_daily/ (accessed on 2 May 2021). CHIRTS: http://data.chc.ucsb.edu/products/CHIRTSdaily/v1.0/ (accessed on 2 May 2021). CRU: https://crudata.uea.ac.uk/cru/data/hrg/ (accessed on 2 May 2021). Acknowledgments: The authors thank the World Climate Research Program for making the CMIP6 dataset available through the Earth System Grid Federation (ESGF) archive and providing free access for this research. The Center for High-Performance Computing (CHPC, Cape town, South Africa) provided the computing facility used for the study. Conflicts of Interest: The authors declare no conflict of interest. Water 2021, 13, 3506 29 of 33 References 1. Peterson, T.; Manton, M. 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