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An accurate analysis of climate extremes is essential for impact assessment and devising appropriate adaptation measures. There is an urgent need to assess precipitation products in capturing the increasing occurrence of climate extremes. This study evaluates the ability of 20 observational datasets, including gauge-based, satellite-based and reanalyses, in representing different drought severity (moderate, severe and extreme drought) over Africa and its nine sub-regions at varying time scales (3-, 6- and 12-months) during 1983–2014. Drought is represented using the Standardized Precipitation Index (SPI). The results demonstrate that while most datasets are suitable for drought studies over the continent, the African Rainfall Climatology version 2 (ARC2) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Records (PERSIANN_CDR_v1r1) are less fitted for such investigations. Moreover, regions such as the Sahara (SAH), Central Africa (CAF) and North Eastern Africa (NEAF) show a larger disparity among the datasets, requiring more caution when selecting a dataset for use in such areas. Generally, the datasets present low agreement toward the lower end of the range (5–30%) because the individual datasets estimate varying drought severities at different grids and months. This is observed in the coefficient of variation of 20–25% of the datasets falling outside the ± 1 standard deviation range. Therefore, using an ensemble to represent the datasets remains an indispensable tool. The datasets present better agreement in the timing of drought events than the spatial distribution. The findings provide valuable insights into the complexity of drought assessment using diverse precipitation datasets. Furthermore, the results highlight the significance of considering spatial and temporal dimensions, as datasets may capture drought events at varying locations and times, revealing subtle variations in drought impact. [ABSTRACT FROM AUTHOR]

This study uses the empirical orthogonal function (EOF) analysis to identify and describe continental‐scale seasonal (MAM, JJA, SON and DJF) precipitation modes over Africa based on observation (Global Precipitation Climatology Centre; GPCC) and reanalysis (ERA5) data from 1982 to 2014. Using composite analysis, we attempt to identify the atmospheric circulations (wind and relative humidity) associated with each precipitation mode. Precipitation from ERA5 has a good agreement with that of GPCC, as observed in the good spatial congruence in the EOF analysis and precipitation composites. The EOF results for each season show that the loading patterns of Mode 1 (variance >20% for all seasons) match the long‐term mean precipitation distribution. Atmospheric conditions across the continent are primarily driven by the four main high‐pressure systems (Azores, St. Helena, Arabian and Mascarene High) that influence moisture distribution and subsequently modulate the seasonal rain belt distribution. Modes 2 (variance >11% for all seasons) and 3 (variance >10% for all seasons) are deviations of the leading mode associated with smaller‐scale atmospheric systems. However, large‐scale factors still dominate the overall precipitation pattern by modulating the seasonal energy transfer between the hemispheres. The results provide a comprehensive understanding of the continental‐wide African seasonal precipitation modes and the associated atmospheric circulation from observations. The findings can be used as a reference for future work using model data for historical and projection studies. [ABSTRACT FROM AUTHOR]

The increasing awareness of climate change requires accurate, reliable and timely information on possible precipitation (PRE) changes to build climate resilience. This study uses the Coupled Model Intercomparison Project phase 6 (CMIP6) data to examine the effectiveness of bias correction on simulated historical mean and extreme PRE, and investigates the projected changes in extreme PRE events over southern Africa (SAF). Quantile mapping on a gamma distribution bias correction method is applied to the historical and projected CMIP6 data, with the Global Precipitation Climatology Centre (GPCC) PRE dataset as reference data. The projection analyses are conducted for the wet (December–March) and dry (May–October) seasons for two shared socioeconomic pathway (SSP) scenarios: SSP2‐4.5 and SSP5‐8.5. The results affirm that the bias correction significantly reduces (by more than 90%) the biases in the modelled mean and extreme PRE. The projected extreme PRE shows a general drying pattern over SAF. In the wet season, wet days (R1mm) are projected to decrease by 1 and 2 days by the end of the 21st century for SSP2‐4.5 and SSP5‐8.5, respectively, while very heavy PRE days (R20mm) show a slight upward pattern over SAF, reaching 0.1 and 0.4 days for the two scenarios. In contrast, the dry season exhibits a more pronounced drying tendency, with a consistent decrease in R1mm of 2 and 0.1 days by 2100 for SSP2‐4.5 and SSP5‐8.5, respectively. R20mm is projected to decrease by 4 and 0.2 days for SSP2‐4.5 and SSP5‐8.5, respectively. This implies that SAF is expected to get drier during the dry season while the PRE intensity during the wet season is projected to increase. [ABSTRACT FROM AUTHOR]

The study aims to analyze climate variability and farmers' perception in Southern Ethiopia. Gridded annual temperature and precipitation data were obtained from the National Meteorological Agency (NMA) of Ethiopia for the period between 1983 and 2014. Using a multistage sampling technique, 403 farm households were surveyed to substantiate farmers' perceptions about climate variability and change. The study applied a nonparametric Sen's slope estimator and Mann–Kendall's trend tests to detect the magnitude and statistical significance of climate variability and binary logit regression model to find factors influencing farm households' perceptions about climate variability over three agroecological zones (AEZs). The trend analysis reveals that positive trends were observed in the annual maximum temperature, 0.02°C/year (p<0.01) in the lowland and 0.04°C/year (p<0.01) in the highland AEZs. The positive trend in annual minimum temperature was consistent in all AEZs and significant (p<0.01). An upward trend in the annual total rainfall (10 mm/year) (p<0.05) was recorded in the midland AEZ. Over 60% of farmers have perceived increasing temperature and decreasing rainfall in all AEZs. However, farmers' perception about rainfall in the midland AEZ contradicts with meteorological analysis. Results from the binary logit model inform that farmers' climate change perceptions are significantly influenced by their access to climate and market information, agroecology, education, agricultural input, and village market distance. Based on these results, it is recommended to enhance farm households' capacity by providing timely weather and climate information along with institutional actions such as agricultural extension services. [ABSTRACT FROM AUTHOR]

This study assesses the performance of 22 Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations of rainfall over East Africa (EA) against reanalyzed datasets during 1951–2005. The datasets were sourced from Global Precipitation Climatology Centre (GPCC) and Climate Research Unit (CRU). The metrics used to rank CMIP5 Global Circulation Models (GCMs) based on their performance in reproducing the observed rainfall include correlation coefficient, standard deviation, bias, percentage bias, root mean square error, and trend. Performances of individual models vary widely. The overall performance of the models over EA is generally low. The models reproduce the observed bimodal rainfall over EA. However, majority of them overestimate and underestimate the October–December (OND) and March–May (MAM) rainfall, respectively. The monthly (inter-annual) correlation between model and reanalyzed is high (low). More than a third of the models show a positive bias of the annual rainfall. High standard deviation in rainfall is recorded in the Lake Victoria Basin, central Kenya, and eastern Tanzania. A number of models reproduce the spatial standard deviation of rainfall during MAM season as compared to OND. The top eight models that produce rainfall over EA relatively well are as follows: CanESM2, CESM1-CAM5, CMCC-CESM, CNRM-CM5, CSIRO-Mk3-6-0, EC-EARTH, INMCM4, and MICROC5. Although these results form a fairly good basis for selection of GCMs for carrying out climate projections and downscaling over EA, it is evident that there is still need for critical improvement in rainfall-related processes in the models assessed. Therefore, climate users are advised to use the projections of rainfall from CMIP5 models over EA cautiously when making decisions on adaptation to or mitigation of climate change. [ABSTRACT FROM AUTHOR]

This study presents potential future variations of mean rainfall and temperature over East Africa (EA) based on five models that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and representative concentration pathways (RCPs): 4.5 and 8.5. In this study, climate simulations of two timeframes, a baseline period (1961–1990) and projection period (2071–2100), are compared. The models reproduce EA's bimodal rainfall pattern but overestimate and underestimate seasonal rainfall of October–December (OND) and March–May (MAM), respectively. Rainfall is projected to increase under the two scenarios. Larger increases in rainfall will occur during the OND season than during the MAM season and in RCP8.5 than in RCP4.5. During the last half of the 21st century, EA is likely to warm by 1.7–2.8 and 2.2–5.4 °C under the RCP4.5 and RCP8.5 scenarios, respectively, relative to the baseline period. Scenario uncertainty is projected to exceed model uncertainty from the middle to the end of the 21st century. The central parts of Kenya and the Lake Victoria Basin will witness the highest increases in seasonal rainfall. The probability density functions (PDFs) of future seasonal rainfall show a positive shift and a statistically insignificant increase in variance relative to the baseline. Thus, EA is likely to experience an increase in extreme rainfall events. Understanding the future climate variability in EA is important for planning purposes but these results are based on relatively course resolution models prone to bias and therefore should be used with caution. There is a need for further research on climate projections over EA, including determining the causes of the poor performance of global models in reproducing rainfall climatology and trends over the region. [ABSTRACT FROM AUTHOR]

The Eastern Africa region experiences frequent extreme precipitation events that can cause destruction of property and environment, and loss of lives. Thus, there is a need to understand how these events may change in the future and how well the global climate models that are used to make projections can simulate precipitation extremes in this region before they can be used in downscaling or flood and drought impact assessment studies. In this work, we evaluated the ability of sixteen Coupled Model Intercomparison Project Phase 6 (CMIP6) models to simulate present-day precipitation extremes over the Eastern Africa region during the two rainy seasons (March–May and September–November). We used nine extreme precipitation indices (including seven (one) indices of wet (dry) extremes) defined by the Expert Team on Climate Change Detection and Indices. The CMIP6 models were evaluated against two gridded observation datasets: Global Precipitation Climatology Project One-Degree Daily Dataset and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis 3B42. Three model performance metrics (percentage bias, normalized root-mean-square error, and pattern correlation coefficient) were employed to further assess the strengths and weakness of the models. Our results show that the multi-model ensemble mean generally provides a better representation of observed precipitation and related extremes compared to individual models when considering all metrics and seasons. Several consistent biases are evident across CMIP6 models, which tend to overestimate the total-wet day precipitation and consecutive wet days, and underestimate very wet days and maximum 5-day precipitation in both seasons. Furthermore, no single model consistently performs best, model performance varies with the season and index under consideration and is generally independent of horizontal resolution. • The CMIP6 models are more consistent with GPCP observation than TRMM. • The CMIP6 multi-model mean performs better than most individual models at capturing extreme precipitation indices. • Overestimate of the total-wet day precipitation and consecutive wet days are evident in both seasons. • No single model consistently performs best, model performance varies with the season and index under consideration. [ABSTRACT FROM AUTHOR]

This study assesses the performance of historical rainfall data from the Coupled Model Intercomparison Project phase 6 (CMIP6) in reproducing the spatial and temporal rainfall variability over North Africa. Datasets from Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) are used as proxy to observational datasets to examine the capability of 15 CMIP6 models' and their ensemble in simulating rainfall during 1951–2014. In addition, robust statistical metrics, empirical cumulative distribution function (ECDF), Taylor diagram (TD), and Taylor skill score (TSS) are utilized to assess models' performance in reproducing annual and seasonal and monthly rainfall over the study domain. Results show that CMIP6 models satisfactorily reproduce mean annual climatology of dry/wet months. However, some models show a slight over/under estimation across dry/wet months. The models' overall top ranking from all the performance analyses ranging from mean cycle simulation, trend analysis, inter-annual variability, ECDFs, and statistical metrics are as follows: EC-Earth3-Veg, UKESM1-0-LL, GFDL-CM4, NorESM2-LM, IPSL-CM6A-LR, and GFDL-ESM4. The mean model ensemble outperformed the individual CMIP6 models resulting in a TSS ratio (0.79). For future impact studies over the study domain, it is advisable to employ the multi-model ensemble of the best performing models. [ABSTRACT FROM AUTHOR]