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This article investigates seasonal and annual trends of near‐surface temperature (NST) during the last century (1901–2018) over Ghana. The ability of 24 global climate models (GCMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to simulate abrupt mean temperature changes in Ghana from 1980 to 2014 was assessed. The trends' magnitude and abrupt changes were detected using Sen Slope Estimator (SSE) and sequential Mann–Kendall (SQMK) tests, respectively. Root mean square difference, correlation coefficient, and mean bias (B) were used to determine the skilfulness of GCMs in simulating NST relative to Climatic Research Unit (CRU TS4.04) datasets. Ghana's Southern belt is characterized by low temperatures, averaging 25°C to 27°C, whilst the northern belt is characterized by high temperatures (29–31°C). The inter‐annual variability of temperature over Ghana exhibits an increasing trend. Findings depict a significant increase in both the annual and seasonal temperatures of Tmin and Tmean, with a steady increase in Tmax. Results revealed the rate of increase had been higher in the country's northern regions (0.5°C) than in the south (0.3–0.4°C) in recent decades. The model performances for the interannual variability of annual and FMAM temperatures are better than their JJAS seasonal performances. The study's overall model ranking shows that Ghana's best performing models for annual, FMAM and JJAS seasons are INM‐CM5‐0, NorCPM1, and KACE‐1‐0‐G, respectively. Observations and comparisons provide useful reference values for a comprehensive assessment of various applications. [ABSTRACT FROM AUTHOR]

Climate change continues to increase the intensity, frequency and impacts of weather and climate extremes. This work uses bias-adjusted Coupled Model Intercomparison Project Phase six (CMIP6) model datasets to investigate the future changes in temperature extremes over Mediterranean (MED) and Sahara (SAH) regions. The mid- (2041–2070) and far-future (2071–2100) are studied under two Shared Socioeconomic Pathways: SSP2-4.5 and SSP5-8.5 scenarios. Quantile mapping function greatly improved the performance of CMIP6 by reducing the notable biases to match the distribution of observation data, the Climate Prediction Center (CPC). Results show persistent significant warming throughout the 21st century, increasing with the increase in radiative forcing. The MED will record a higher increase in temperature extremes as compared to SAH. The warming is supported by the projected reduction in cold days (TX10p) and cold nights (TN10p), with the reduction in the number of cold nights exceeding cold days. Notably, warm spell duration index (WSDI) and summer days (SU) have a positive trend in both timelines over the entire study area. There is a need to simulate how climate sensitive sectors, such as water and agriculture, are likely to be affected by projected changes under different scenarios for informed decision making in the choice and implementation of adaptation and mitigation effective measures. [ABSTRACT FROM AUTHOR]

This study employed 15 CMIP6 GCMs and evaluated their ability to simulate rainfall over Uganda during 1981–2014. The models and the ensemble mean were assessed based on the ability to reproduce the annual climatology, seasonal rainfall distribution and trend. Statistical metrics used include mean bias error, normalized root mean square error, and pattern correlation coefficient. The Taylor diagram and Taylor skill score (TSS) were used in ranking the models. The models' performance varies greatly from one season to the other. The models reproduced the observed bimodal rainfall pattern of March to May (MAM) and September to November (SON) occurring over the region. Some models slightly overestimated, while some slightly underestimated, the MAM rainfall. However, there was a high rainfall overestimation during SON by most models. The models showed a positive spatial correlation with observed dataset, whereas a low correlation was shown inter‐annually. Some models could not capture the rainfall patterns around local‐scale features, for example, around the Lake Victoria basin and mountainous areas. The best performing models identified in the study include GFDL‐ESM4, CanESM5, CESM2‐WACCM, MRI‐ESM2‐0, NorESM2‐LM, UKESM1‐0‐LL, and CNRM‐CM6‐1. The models CNRM‐CM6‐1, and CNRM‐ESM2 underestimated rainfall throughout the annual cycle and mean climatology. However, these two models better reproduced the spatial trends of rainfall during both MAM and SON. Caution should be taken when employing the models in seasonal climate change studies as their performance varies from one season to another. The model spread in CMIP6 over the study area also calls for further investigation on the attributions and possible implementation of robust approaches of machine learning to minimize the biases. [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]

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]

This paper presents an analysis of projected precipitation extremes over the East African region. The study employs six indices defined by the Expert Team on Climate Change Detection Indices to evaluate extreme precipitation. Observed datasets and Coupled Model Intercomparison Project Phase six (CMIP6) simulations are employed to assess the changes during the two main rainfall seasons: March to May (MAM) and October to December (OND). The results show an increase in consecutive dry days (CDD) and decrease in consecutive wet days (CWD) towards the end of the 21st century (2081–2100) relative to the baseline period (1995–2014) in both seasons. Moreover, simple daily intensity (SDII), very wet days (R95 p), very heavy precipitation >20 mm (R20 mm), and total wet-day precipitation (PRCPTOT) demonstrate significant changes during OND compared to the MAM season. The spatial variation for extreme incidences shows likely intensification over Uganda and most parts of Kenya, while a reduction is observed over the Tanzania region. The increase in projected extremes may pose a serious threat to the sustainability of societal infrastructure and ecosystem wellbeing. The results from these analyses present an opportunity to understand the emergence of extreme events and the capability of model outputs from CMIP6 in estimating the projected changes. More studies are recommended to examine the underlying physical features modulating the occurrence of extreme incidences projected for relevant policies. [ABSTRACT FROM AUTHOR]

The future planning and management of water resources ought to be based on climate change projections at relevant temporal and spatial scales. This work uses the new regional demarcation for Southern Africa (SA) to investigate the spatio-temporal precipitation variability and trends of centennial-scale observation and modeled data, based on datasets from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The study employs several statistical methods to rank the models according to their precipitation simulation ability. The Theil–Sen slope estimator is used to assess precipitation trends, with a Student's t-test for the significance test. The comparison of observation and model historical data enables identification of the best-performing global climate models (GCMs), which are then employed in the projection analysis under two Shared Socioeconomic Pathways (SSPs): SSP2-4.5 and SSP5-8.5. The GCMs adequately capture the annual precipitation variation but with a general overestimation, especially over high-elevation areas. Most of the models fail to capture precipitation over the Lesotho-Eswatini area. The three best-performing GCMs over SA are FGOALS-g3, MPI-ESM1-2-HR and NorESM2-LM. The sub-regions demonstrate that precipitation trends cannot be generalized and that localized studies can provide more accurate findings. Overall, precipitation in the wet and dry seasons shows an initial increase during the near future over western and eastern SA, followed by a reduction in precipitation during the mid- and far future under both projection scenarios. Madagascar is expected to experience a decrease in precipitation amount throughout the twenty-first century. [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]

This work employed recent model outputs from coupled model intercomparison project phase six to simulate surface mean temperature during the June–July–August (JJA) and December–January–February (DJF) seasons for 1970–2014 over Pakistan. The climatic research unit (CRU TS4.03) dataset was utilized as benchmark data to analyze models' performance. The JJA season exhibited the highest mean temperature, whilst DJF displayed the lowest mean temperature in the whole study period. The JJA monthly empirical cumulative distribution frequency (ECDF) range (26 to 28 °C) was less than that of DJF (7 to 10 °C) since JJA matched closely to CRU. The JJA and DJF seasons are warming, with higher warming trends in winters than in summers. On temporal scale, models performed better in JJA with overall low bias, low RMSE (root mean square error), and higher positive CC (correlation coefficient) values. DJF performance was undermined with higher bias and RMSE with weak positive correlation estimates. Overall, CanESM5, CESM2, CESM2-WACCM, GFDL-CM4, HadGEM-GC31-LL, MPI-ESM1-2-LR, MPI-ESM1-2-HR, and MRI-ESM-0 performed better for JJA and DJF. [ABSTRACT FROM AUTHOR]

Extreme high temperature (EHT) events are the major indicator of global warming and their effect on natural ecosystems and agricultural production. The present study investigates the historical changes and projected trends of EHT events using Coupled Model Intercomparison Project phase 6 (CMIP6) Global Climate Models (GCMs) and observation data in East Africa. The distribution mapping (DM) approach effectively reduces the biases and improves the CMIP6 GCMs to match the observation data. This study considers the intensity of hot days (TXx) and hot nights (TNx) and two frequency indices estimated from the 90th percentile of the hot days (TX90p) and hot nights (TN90p) to evaluate the EHT events. The Mann-Kendall (MK) test and Sens's slope were used to estimate the trend of the EHT indices and significance level. Moreover, a regression approach was utilized to examine the relationship between EHT indices and meteorological conditions during the historical period (1990–2011). The results demonstrate that the recent period (2001−2010) experienced more frequent hot days (∼10–15%) and nights (∼12–20%) compared to the first decade (∼0–7%) (1990–2000) in most of the East Africa region. In addition, most stations across East Africa show a significant positive trend for the frequency of hot nights. The ensemble of the CMIP6 model simulation showed that the EHT events were projected to continue rising during the end of the 21st century (2071–2095). The intensity of hot days and nights is projected to escalate between 1.5 and 3 °C and 1–4 °C under SSP2–4.5 and SSP5–8.5 scenarios. Although, a high upsurge was detected in the frequency of hot nights (∼23–30%) during far future (2071–2095) compared to mid-future (2041–2070) under SSP5–8.5 and SSP2–4.5 scenarios. In addition, a strong correlation (R2 = ±0.8) was found between meteorological conditions and EHT indices, indicating the influence of atmospheric circulation on extreme temperature over East Africa. Our study provides valuable information regarding developing new policies and early warning systems for water resource managers and policymakers. • The historical and future changes in EHT events were assessed based on CMIP6 GCMs over East Africa. • The number of hot nights increased at higher rate than hot days over East Africa. • The frequency of hot nights upsurge during the recent decade (2001–2010) compared to 1st decade (1991–2000) in East Africa. • Kenya and Tanzania are projected to warm faster compared to Sudan and Ethiopia during the end (2071–2095) of the 21st century under SSP2–4.5. [ABSTRACT FROM AUTHOR]