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Over the past few decades, South Asia (SA) has experienced an upsurge in the frequency of severe monsoonal compound drought and heatwave (CDHW) occurrences. Climate models that identify land‐atmosphere coupling as a major contributing factor for this exacerbation and anticipate an increase in the intensity and frequency of CDHW occurrences in future also represent this. For the first time, this study investigated the future evolution of monsoonal CDHW events based on new generations of the CMIP6 and population products by applying a multivariate framework. Specifically, this study explored the impacts of natural climate variability and future land‐atmosphere coupling on the monsoonal CDHW event risks and their bivariate return periods for two future time‐periods and emission scenarios across SA and its subregions. The odds of CDHW occurrences were then examined using a logistic regression model and its association with the natural and anthropogenic drivers was determined. The results indicate that the monsoonal CDHWs occurrence is anticipated to increase substantially during the late twenty‐first century (2056–2090). The 50‐year CDHW events might increase by two‐fold across most of SA by the mid‐21st century under the high emission scenario. We find that the co‐occurring dry and warm conditions rapidly strengthens with soil moisture and temperature coupling and are further exacerbated by land‐atmospheric feedback loops. Our findings show that persistent dry spells contribute significantly to heatwave events, emphasizing regional exposure to changing climates. Plain Language Summary: CDHW (compound drought and heatwave) events may have caused more severe effects on agriculture, water resources, and human society than their occurrences. Investigating future changes in CDHW is critical for anticipating and reducing the adverse effects of climate change and variability, especially in a densely populated region like South Asia (SA), where most of the population still depends on agricultural productivity. Here, we propose a novel compound and multivariate risk assessment framework to capture weekly droughts and heatwaves at a daily time scale using advanced scientific methods. With this objective, the outcomes of the current study can directly map the vulnerable areas in SA and expose the relationship between two events (drought and heatwave). The current research also explores the risk and population exposure due to CDHW events across SA under current and future warming climates by considering the joint distribution of CDHW severity and duration. The study findings will help inform stakeholders about vulnerable areas where the CDHW events are more likely to become more frequent and severe due to climate change. Key Points: CDHW occurrences are projected to increase significantly during the period mid and late 21st century under the business‐as‐usual scenarioThe population exposure from 50‐year CDHW event might increase two‐fold across 70% of the SA landmasses already by the mid‐21st centuryAnthropogenic global warming show significant positive influence on the most vulnerable climatic regions [ABSTRACT FROM AUTHOR]

The present study evaluates the performance of high‐resolution global climate models derived from Coupled Model Intercomparison Project Phase 6 (CMIP6 HighResMIP), in simulating rainfall characteristics over Madagascar on an annual and seasonal scales for the period 1981–2014. The models and their ensemble mean are assessed based on two observational datasets sourced from Climate Hazards Group Infrared Precipitation with Station data version 2 (CHIRPS v2.0) data and the European Centre for Medium‐Range Weather Forecasts (ECMWF) reanalysis fifth generation‐Land dataset (ERA5) as the references throughout the diverse analyses. A Taylor diagram, accompanied by the Taylor skill score (TSS), is used for the annual and seasonal model‐rankings and the overall performance of the models. The best‐performing models are EC‐Earth3P‐HR, ECMWF‐IFS‐HR, ECMWF‐IFS‐LR and HadGEM3‐GC31‐MM. The least‐recommended models with remarkable biases are BCC‐CSM2‐HR, CAMS‐CSM1‐0, FGOALS‐f3‐H, MPI‐ESM1‐2‐HR and MPI‐ESM1‐2‐XR. It is worth mentioning that FGOALS‐f3‐H tends to overestimate rainfall in most analyses, while MPI‐ESM1‐2‐HR and MPI‐ESM1‐2‐XR underestimate it. The findings of this study are of great importance to climatologists and present an opportunity for further investigation of underlying processes responsible for the observed wet/dry biases in order to improve the forecast skills in the models over the study area. [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 selection of a reliable long‐term rainfall dataset is deemed appropriate for hydroclimatic assessments, especially in regions with limited distribution of rain gauge stations like Sudan. The study presents an initial investigation for gridded‐based rainfall products and satellite‐based products over Sudan using standardized statistical methods. Overall, the monthly evaluation revealed reasonable statistical agreement with in situ observations. Seasonal analysis shows that summer rainfall is detected more accurately than yearly rainfall, especially in mountainous areas across the country. Generally, underestimation was observed in all products over most regions on an annual, seasonal, and monthly scale, respectively. Results for the Taylor skill score (TSS) indicated relatively low skills for ECMWF Reanalysis 5th Generation (ERA5), moderate to strong skills for Climate Research Unit (CRU) and Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), against the reference rain gauges among the regions. The regional comparison using TSS when considering monthly and seasonal timescales showed CHIRPS and CRU had the highest performance with an average percentage of 89.6 and 92% in the west (Nyala, Al Geniena, and Al Fasher), 89 and 84.6% at the central part (Khartoum, Wad Madani, and Al Obied), and 92.5 and 86.5% in the south (Al Damazine and Kadogli). At 95% confidence level, a significant increasing trend was observed over the highlands at the centre (Al Obied) and western areas (Al Geniena and Nyala), while most stations showed positive trends. Further analysis on temporal correlation with Atlantic Multidecadal Oscillation (AMO) demonstrated a strong correlation between the AMO PC1 and JJAS rainfall PC1 (82%). The west recorded the highest response to AMO (90%), while the east had the lowest response (74%), with the north, central, and south obtaining 77, 76, and 76%, respectively. Our findings provide useful information about rainfall in Sudan, suggesting CHIRPS data for monitoring rainfall variability and extreme events which should encourage its use in areas with scarce stations. [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]

This study examines the projected changes in mean and extreme precipitation over the Mediterranean (MED) and Sahara (SAH) regions based on the multi‐model ensemble mean of the Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate model (GCM) datasets. The study employs robust statistical analyses to investigate future changes during 2015–2100 relative to a baseline period (1995–2014), under two Shared Socio‐economic Pathways (SSP) scenarios: SSP2‐4.5 and SSP5‐8.5. Selected indices from the Expert Team on Climate Change and Detection Indices are used in this study. They include those that represent maximum daily precipitation (RX1day), simple daily precipitation intensity (SDII), heavy precipitation days (R10mm), consecutive dry days (CDD), and consecutive wet days (CWD). Historical and projected daily precipitation is first bias‐adjusted using a quantile mapping approach before employing them to compute mean and extreme precipitation changes. The results demonstrate that the bias adjustment largely reduces the biases in the modelled mean and extreme precipitation over MED and SAH regions. Projections show a reduction in mean precipitation over most parts of the study region by the end of the 21st century. The areas encompassing Morocco and Algeria, and the Mediterranean area will experience the highest drying. The projected pattern agrees with the "wet gets wetter, dry gets drier" paradigm. The number of consecutive dry days and wet‐day intensity are also projected to increase and decrease, respectively. Under SSP5‐8.5, significant changes and the largest decrease in SDII attributed to global warming are projected in both regions. The reduction in mean precipitation, coupled with an increase in dry days, is likely to exacerbate the region's droughts and aridity situation and worsen the water scarcity status. Although there are uncertainties in the CMIP simulations, the findings support earlier studies based on varying datasets. This increases confidence in the output for decision‐making. [ABSTRACT FROM AUTHOR]

Madagascar is among the countries whose agriculture is heavily dependent on rainfall. However, the country lacks accurate and reliable early warning systems for droughts and floods, partly due to insufficient station rainfall data. The purpose of this study is to identify rainfall datasets that can complement observation data by appraising 15 datasets (gauge‐based, reanalysis, and satellite estimates). The study compares the temporal and spatial performance of datasets at annual and seasonal scales during 1983–2015. In all the analyses, CHIRPS presents lower biases, so it is chosen as the reference data in the Taylor diagram for the final evaluation analysis. Even though ranking datasets is neither possible nor appropriate since each dataset performs differently throughout each analysis, some datasets show reasonable consistency. This is the case with MSWEP, ERA5, and UDEL. On the other hand, MERRA2, CMAP, and TAMSAT are least preferred for use due to their considerable biases (specifically TAMSAT during the dry season). CRU, PRECL, ERAINT, CFSR, and JRA55 also present some degrees of deficiencies at either annual or seasonal scales. These findings are crucial for any future rainfall analysis over the country in order to minimize inaccuracy in monitoring rainfall. [ABSTRACT FROM AUTHOR]

More frequent and intensified high‐temperature extremes are acceptable indicators of global warming, which pose serious socio‐economic impacts. In the present research, the Climate Prediction Center daily minimum and maximum temperature are used to characterize high temperatures into intensity of hot days (TXx), hot nights (TNx), and frequency indices based on the 90th percentile of hot days (TX90p) and hot nights (TN90p) as defined by the Expert Team on Climate Change Detection and Indices from 1981 to 2020 over Africa. The regression approach based on iterative reweighted least squares and correlation analyses are used to estimate the trends in extreme high‐temperature events and their relationship with various meteorological variables. Furthermore, the Pruned exact linear time algorithm is used to assess if the maximum temperature experienced an abrupt change. Results show that many parts of Africa experienced more frequent and intensified hot days and nights in the current period (1998–2020) compared to the recent past (1981–1997), suggesting a clear shift in climate. Thus, we used the climatological mean differences between meteorological parameters before and after the breakpoint to assess the relationship between atmospheric conditions and extreme high‐temperature events. As a result, we found that the current period observed an increase in the geopotential height at 500 hPa and reduced total cloud cover, as well as an increase in upward longwave radiation resulting in an upsurge in the frequency of hot days over many African sub‐regions. The present study's findings provide useful information for future planning and development of early warning systems to cope with risks associated with hot extremes. [ABSTRACT FROM AUTHOR]

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]

This study examines the improvement in Coupled Model Intercomparison Project Phase Six (CMIP6) models against the predecessor CMIP5 in simulating mean and extreme precipitation over the East Africa region. The study compares the climatology of the precipitation indices simulated by the CMIP models with the CHIRPS data set using robust statistical techniques for 1981–2005. The results display the varying performance of the general circulation models (GCMs) in the simulation of annual and seasonal precipitation climatology over the study domain. CMIP6 multi‐model ensemble mean (hereafter MME) shows improved performance in the local annual mean cycle simulation with a better representation of the rainfall within the two peaks, especially the MAM rainfall relative to their predecessor. Moreover, simulation of extreme indices is well captured in CMIP6 models relative to CMIP5. The CMIP6‐MME performed better than the CMIP5‐MME with lesser biases in simulating Simple Daily Intensity Index (SDII), consecutive dry days (CDD), and very heavy precipitation days >20 mm (R20mm) over East Africa. Remarkably, most CMIP6 models are unable to simulate extremely wet days (R95p). Some CMIP6 models (e.g., NorESM2‐MM and CNRM‐CM6‐1) depict robust performance in reproducing the observed indices across all analyses. OND season shows wet biases for some indices (i.e., R95p, PRCPTOT), except for SDII, CDD, and R20mm in CMIP6 models. Consistent with other studies, the mean ensemble performance for both CMIP5/6 shows better performance as compared with individual models due to the cancellation of some systematic errors in the individual models. Generally, CMIP6 depicts improved performance in the simulation of MAM rainfall compared with CMIP5 models. However, the new model generation is still marred by uncertainty, thereby depicting unsatisfactory performance over the East Africa domain. This calls for further investigation into the sources of persistent systematic biases and a methodology for identifying individual models with robust features that can accurately simulate observed patterns for future usage. [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]

Climatic change over the globe due to global warming affects the characteristics of climate variables that have critical implications on large fraction of population that depends on agriculture for livelihood like Pakistan. Consequently, this study examined how high horizontal grid resolution CMIP6 models simulate the observed precipitation variability during 1981–2014 and further explored the future changes during 2017–2050 under high emission scenario SSP5‐8.5 over Pakistan region. The performances of 12 (CMIP6) High Resolution Model Inter‐comparison Project version 1.0 (hereafter; HighResMIP) GCMs and their ensemble means in reproducing the observed climate were calculated at each station in the study domain and formed the basis for deriving HighResMIP ranking. Further, the study employed Shannon's Entropy and a modified version of Criteria Importance through Inter‐Criteria Correlation (D‐CRITIC) method to build an ensemble mean from the best performing models. Evaluation of HighResMIP GCMs performance revealed that most models showed mixed signals in the region, with fewer models such as HadGEM3‐GC31‐HH, HadGEM3‐GC31‐HM and HadGEM3‐GC31‐MM showing good agreement with the observed precipitation. Overall, HighResMIP multi‐model ensemble outperforms precipitation distribution over individual models. D‐CRITIC based ensemble mean implies higher increase in precipitation than entropy approach. Future changes depict an increase in mean annual in the northern region relative to the historical period. A pronounced increase of about 16%–18% in precipitation was noted in HadGEM3‐GC31‐HH and HiRAM‐SIT‐HR. Conversely, FGOAL‐f3‐H project noteworthy reduction (21%) in precipitation in the near future (2017–2050). The projected seasonal precipitation shows upsurge pattern of 5%–28% in pre‐monsoon season, whereas the reduction in monsoon precipitation is projected to be 29%–40%. The findings of this study can help in building future climate resilience and developing strategic policies in Pakistan. [ABSTRACT FROM AUTHOR]