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The paucity of reliable ground based datasets remains a major challenge over Kenya. In the advent of extreme wetness or drought events, reliable precipitation estimates for local characterization is a long overdue process. In the present study, four Satellite derived Precipitation Estimates (SPE): TMPA V7 3B42, PERSIANN-CDR, CHIRPS, and ARC2, are assessed over four homogeneous zones in Kenya with gauge based data during 1998–2016. Results show that variations of SPE products are based on complex geomorphology of different climatic zones. All SPE products depict bimodal annual precipitation pattern with west-east gradient representing heavier to lighter precipitation events. The Monthly analysis reveal good statistical agreement with reference datasets despite underestimation of precipitation in most regions. Seasonal precipitation events show that the PERSIANN-CDR perform better along low altitude humid climate and western zones around Lake Basin while ARC2 has uniform performance as gauge stations over highlands regions. Strong positive linear relationship on annual scale is evident in most SPE products with CHIRPS, ARC2, and TMPA exhibiting relatively high correlation (r) and minimum root mean square error (RMSE), except for PERSIANN-CDR. Overall, the findings of this study show the potentials of SPE products for applications over study domain. The TMPA V7 and PERSIANN-CDR could be useful in understanding individual floods events. Since the CHIRPS perform relatively well over ASAL regions, it could be utilized in monitoring droughts events. • Four Satellites derived Precipitation Estimates (SPE): TMPA V7 3B42, PERSIANN-CDR, CHIRPS, and ARC2, are assessed over four homogeneous zones in Kenya. • All SPE products depict a bimodal pattern of climatology with west-east gradient representing heavier to lighter precipitation events. • Monthly analyses reveal good statistical agreement with reference datasets by TMPA, ARC2, and CHIRPS despite underestimation of precipitation in most regions. [ABSTRACT FROM AUTHOR]

The variability in the diurnal temperature range (DTR), an indicator of climate change, remains limited, especially over Africa, due to the scarcity of observed maximum and minimum temperature data. This work investigates the ability of the Coupled Model Intercomparison Project (CMIP6) to simulate DTR over Africa for the period 1980–2014. Datasets from the Climatic Research Unit (CRU TS4.05) and National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Centre (CPC) gridded temperature datasets are utilized as observed data. Similar to the high variability in topography and climate across the continent, the DTR exhibits high heterogeneity over Africa. The Sahara and its environs record the highest DTR, while Central Africa and coastal areas experience the least, given the thermal inertia of water bodies. CMIP6 models overestimate and underestimate DTR over different parts of the continent. Moreover, the multi-model ensemble mean of CMIP6 models shows significant decreasing trends both in seasonal and annual trends. Overall, five CMIP6 models such as EC-Earth3, ACCESS-CM2, BCC-CSM2-MR, EC-Earth3-veg, and IPSL-CM6A-LR show robust skill scores (0.48–0.54). The findings form the basis for investigating the role of temperature extremes on DTR. Further, the variability in DTR across parts of the continent prompts the need for future assessments to investigate future changes in DTR. • The performance of 22 CMIP6 models to simulate the climatology and trends of DTR is comprehensively investigated over Africa. • The African continent is divided into 9 subrings and two observation datasets (CRU and CPC) are considered during 1980–2014. • The multi-model ensemble mean of CMIP6 models shows significant decreasing trends both in seasonal and annual trends. • The best-performing models are: CMIP6 models such as EC-Earth3, ACCESS-CM2, BCC-CSM2-MR, EC-Earth3-veg, and IPSL-CM6A-LR. [ABSTRACT FROM AUTHOR]

Diurnal temperature range (DTR) is one of the key indicators of global climate change. In this study, a multi-model ensemble (MME) of five best-performing models over Africa from Coupled Model Intercomparison Project Phase 6 (CMIP6) under two socioeconomic scenarios (SSP2-4.5 and SSP5-8.5) were employed to compute the spatial variability of the future (2015–2100) DTR relative to the baseline period (1980–2014). The Modified Mann-Kendall Test (mMK) was used to analyze DTR trends, while Theil-Sen's slope estimator was used to assess the magnitude and significance of changes in future DTR. Boxplot plots are used to estimate the uncertainty of future trends relative to the baseline period. Results reveal that the annual DTR over most regions in Africa will decline under SSP5-8.5 scenarios except for the SWAF domain, where a slight increase is projected to occur. Likewise, the seasonal anomalies for DJF present a consistent decline in DTR over SAH (−0.2 °C), WAF (−0.8 °C), CAF (−0.4 °C), NEAF (−0.6 °C), and SEAF (−0.2 °C) under SSP5-8.5. Moreover, the JJA season showed a clear decline under SSP5-8.5 of up to −0.8 °C over CAF after 2050. Most of the continent is likely to experience a significant trend of −0.1 to −0.4/year. Furthermore, the CAF and NEAF regions showed a significant decline (−2.4/year) in DTR under both scenarios of SSP2-4.5 and SSP5-8.5 across the months and years. Large uncertainty is recorded during the DJF season and more predominately over the NEAF, SEAF, WAF, and CAF regions, characterized by negative skews (−0.018 °C/year) and large interquartile ranges (−0.007 to −0.024) in both timescales. Future studies on the projected DTR may focus on the impacts of the variability of the DTR on sectors such as health and morbidity, crop yields, impact assessments, etc. • The five best-performing CMIP6 models were employed to compute the ensemble projection over Africa. • The study area was divided into 9 sub-regions and two socioeconomic scenarios (SSP2-4.5 and SSP5-8.5) were considered. • Results reveal that the annual DTR over most regions in Africa will decline under SSP5-8.5. • Most of the continent is likely to experience a significant trend of −0.1 to −0.4/year. [ABSTRACT FROM AUTHOR]

• Crop production at various scales is constrained by climatic and non-climatic variables. • In Morocco wheat stands out as the most studied crop. • AquaCrop process-based crop model and regression-based models are the most frequently calibrated crop models. • Precipitation, temperature, soil properties, irrigation, and fertilization are frequently calibrated indicators. • Empirical models aptly integrate climatic and socioeconomic variables. • Process-based models focus on mostly climatic and biophysical variables. Agriculture is an important sector of the Moroccan economy, employing a huge portion of the Moroccan population and contributing about 14 - 20% to the country's GDP. Unfortunately, agricultural production in Morocco is impacted by climatic, non-climatic, biophysical, and non-biophysical stressors. Researchers have employed various crop models to understand how different crops respond to different environmental conditions such as temperature, precipitation, soil properties, fertilization, and irrigation. Unfortunately, there are no studies that provide a summary and a holistic perspective of the most frequently used models and their calibration inputs in Morocco. This work, therefore, seeks to fill these knowledge gaps by providing a summary of the most calibrated crop models, their calibration input data, the most frequently studied crops, how the studies are published (peer-review or grey literature), and the affiliations of the lead authors. This is achieved through a systematic review of the primary peer review and grey literature. A total of 68 relevant peer review and grey literature papers were considered. The results show that most of the authors are affiliated with Moroccan universities/organizations while wheat is the most studied crop. In addition, the AQUACROP and the regression-based models are the most used crop models. Additionally, most of the models are calibrated in order of importance with variables such as temperature, precipitation, soil properties, irrigation, and fertilizers. On the other hand, there is an observed increase in the use of non-climatic indicators such as poverty, farm income, and literacy levels to fit empirical models. It is still unclear how process-based models will integrate socio-economic indicators. This work has implications for future research as it provides a holistic picture of the key models that are currently used and their calibration. This information can be used by other projects to select methods to use, and crops to study based on the available data when working on crop models in Morocco, and North Africa. These results underscore the leading role in research funding offered by the government of Morocco and other organizations such as UM6P and OCP Africa in research valorization in Morocco and Africa. [ABSTRACT FROM AUTHOR]

This study evaluates the historical mean surface temperature (hereafter T2m) and examines how T2m changes over East Africa (EA) in the 21st century using CMIP6 models. An evaluation was conducted based on mean state, trends, and statistical metrics (Bias, Correlation Coefficient, Root Mean Square Difference, and Taylor skill score). For projections over EA, five best performing CMIP6 models (based on their performance ranking in historical mean temperature simulations) under the shared socioeconomic pathways SSP2-4.5 and SSP5-8.5 scenarios were employed. The historical simulations reveal an overestimation of the mean annual T2m cycle over the study region with fewer models depicting underestimations. Further, CMIP6 models reproduce the spatial and temporal trends within the observed range proximity. Overall, the best performing models are as follows: FGOALS-g3, HadGEM-GC31-LL, MPI-ESM2-LR, CNRM-CM6-1,andIPSL-CM6A-LR. During the three-time slices under consideration, the Multi Model Ensemble (MME) project many changes during the late period (2080–2100) with expected mean changes at 2.4 °C for SSP2-4.5 and 4.4 °C for the SSP5-8.5 scenario. The magnitude of change based on Sen's slope estimator and Mann-Kendall test reveal significant increasing tendencies with projections of 0.24 °C decade-1 (0.65 °C decade-1) under SSP2-4.5(SSP5-8.5) scenarios. The findings from this study illustrate higher warming in the latest model outputs of CMIP6 relative to its predecessor, despite identical instantaneous radiative forcing. • Historical and future changes in mean surface temperature is examined using CMIP6 models in EA region. • Best models include FGOALS-g3, HadGEM-GC31-LL, MPI-ESM2-LR, CNRM-CM6-1, and IPSL-CM6A-LR. • Projection shows mean changes at 2.4 °C (4.4 °C) for SSP2-4.5 (SSP5-8.5) scenarios during 2080–2100. • Significant increasing trends at 0.24 °C (0.65 °C decade-1) under SSP2-4.5 (SSP5-8.5) scenarios. • The study shows higher warming in CMIP6 over the study region. [ABSTRACT FROM AUTHOR]

Understanding future evolution of drought scenario across localized domains remains an imperative process in bid to adapt tailor suit innovative solutions to drought risks and their impacts. The present study examines drought events by characterizing the trend, intensity, severity and frequency based on Standardized Precipitation Index (SPI), over Kenya, East Africa for near future (2010–2039), mid-century (2040–2069), and late century (2070–2100). The study utilizes Multi-model mean ensemble (MME) of five selected regional climate models (RCMs). Further, the models are bias corrected based on a quantile mapping bias corrected algorithm in order to minimize possible bias for accurate projections. The changes in annual and seasonal precipitation over Kenya is examined in order to associate with changes in drought occurrence. Results demonstrate positive shift, indicating an increase in projected rainfall change during all the three timescales. Projections of possible future meteorological drought events under RCPs scenario over study locale was conducted using SPI. The results demonstrate relatively better performance of biased corrected MME derived from Rossby Centre regional climate model (RCA4) in simulating drought indices over the Kenya. The MME projections for drought duration show an increase in moderate drought incidences with fewer incidences of extreme events across the RCP4.5 and 8.5 scenarios respectively. However, the duration of occurrences varies from one region to another with most hotspots located around northeastern sides of the country. Examination of projected changes in drought frequency and severity depict an occurrence of severe to extreme drought incidences that are expected to intensify during the near future time slice while overall projections show that more wet scenarios is depicted, with fewer cases of drought expected to occur during mid and towards end of the century of projection period. The study calls for enactment of appropriate mitigation measures to cope with possible scenarios of drought risks over Kenya in the future. • The study examines drought trends, intensity, severity and frequency using SPI over Kenya • Projections for drought show an increase in moderate drought incidences for RCP4.5 and RCP8.5. • The duration of drought occurrences how hotspot located over northeastern region of the study area. • The overall projections show that more wet scenario is depicted, with fewer cases of drought towards the end of the century [ABSTRACT FROM AUTHOR]

This study assesses the performance of ten Regional Climate Model (RCMs) from the latest version of Rossby Centre of Atmospheric models (RCA4) in the simulation of precipitation over Greater Horn of Africa (GHA) from 1951–2005. The evaluation was performed against observed data from the Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC). Results for mean seasonal analyses demonstrate an underestimation of March–May (MAM) and June–September (JJAS) precipitation whilst October to December (OND) precipitation is overestimated. Further assessment on the annual scale depicts underestimation of rainfall. However, the west to east gradient representing heavier to lighter precipitation and bimodal patterns of the north to south rainfall band is well captured by most models. The models fairly reproduce precipitation variability over the southeast region as compared to the northwest parts of the study domain. The mean ensemble invariably outperforms the individual RCA4 models due to its minimal probability deviance in precipitation in each zone and throughout the GHA region. The overall evaluation shows weak correspondence of the model data with observed CRU based on statistical metrics. The top five performing models are: MIROC5, CSIRO, CM5A-MR, MPI-ESM-LR, and EC-EARTH. Large variations of model performance are noted from one model to model, and from one region to the other. The ensemble mean of the outperforming RCMs reproduces the rainfall climatology over study domain with reasonable skill and the findings of this study will be a base for the study of extreme floods/droughts events in the region. • This study appraise the performance ten Rossby Centre of Atmospheric models (RCA4) in the simulation of precipitation over GHA domain during 1951-2005 • Results for mean seasonal analyses demonstrate an overestimation of OND and JJAS rainfall whilst an underestimation amidst the MAM season. • Annual scale depicts underestimation of rainfall. • The overall evaluation shows weak correspondence of the model data with observed CRU based on statistical metrics. • The best five models are as follows: MIROC5, CSIRO, CM5A-MR, MPI-ESM-LR, and EC-EARTH. [ABSTRACT FROM AUTHOR]