Global warming induces spatially heterogeneous changes in precipitation patterns, highlighting the need to assess these changes at regional scales. This assessment is particularly critical for Afghanistan, where agriculture serves as the primary livelihood for the population. New global climate model (GCM) simulations have recently been released for the recently established shared socioeconomic pathways (SSPs). This requires evaluating projected precipitation changes under these new scenarios and subsequent policy updates. This research employed six GCMs from the CMIP6 to project spatial and temporal precipitation changes across Afghanistan under all SSPs, including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The employed GCMs were bias-corrected using the Global Precipitation Climatological Center's (GPCC) monthly gridded precipitation data with a 1.0° spatial resolution. Subsequently, the climate change factor was calculated to assess precipitation changes for both the near future (2020–2059) and the distant future (2060–2099). The bias-corrected projections' multi-model ensemble (MME) revealed increased precipitation across most of Afghanistan for SSPs with higher emissions scenarios. The bias-corrected simulations showed a substantial increase in summer precipitation of around 50%, projected under SSP1-1.9 in the southwestern region, while a decline of over 50% is projected in the northwestern region until 2100. The annual precipitation in the northwest region was projected to increase up to 15% for SSP1-2.6. SSP2-4.5 showed a projected annual precipitation increase of around 20% in the southwestern and certain eastern regions in the far future. Furthermore, a substantial rise of approximately 50% in summer precipitation under SSP3-7.0 is expected in the central and western regions in the far future. However, it is crucial to note that the projected changes exhibit considerable uncertainty among different GCMs.

Climate change has significantly increased human thermal stress, particularly in tropical regions, exacerbating associated risks and consequences, such as heat-related illnesses, decreased workability, and economic losses. Understanding the changes in human thermal stress and its drivers is crucial to identify adaptation measures. This study aims to assess various meteorological variables’ spatial and seasonal impact on Wet Bulb Globe Temperature (WBGT), an indicator of human thermal stress, in Peninsular Malaysia. The Liljegren method is used to estimate WBGT using ERA5 hourly data from 1959 to the present. The trends in WBGT and its influencing factors are evaluated using a modified Mann-Kendall test to determine the region’s primary driver of WBGT change. The results indicate that air temperature influences WBGT the most, accounting for nearly 60% of the variation. Solar radiation contributes between 20% and 30% in different seasons. Relative humidity, zenith, and wind speed have relatively lesser impacts, ranging from −5% to 20%. Air temperature has the highest influence in the northern areas (>60%) and the lowest in the coastal regions (40%). On the other hand, solar radiation has the highest influence in the southern areas (20–40%) and the least in the north. The study also reveals a significant annual increase in temperature across all seasons, ranging from 0.06 to 0.24 °C. This rapid temperature rise in the study area region has led to a substantial increase in WBGT. The higher increase in WBGT occurred in the coastal regions, particularly densely populated western coastal regions, indicating potential implications for public health. These findings provide valuable insights into the factors driving WBGT and emphasize the importance of considering air temperature as a key variable when assessing heat stress.

Climate change poses significant risks to Sarawak, Malaysia, a region renowned for its ecological diversity, where accurate projections of temperature patterns are essential for effective adaptation and resource management. Existing studies often rely on limited global climate models (GCMs) without systematic evaluation, and the high variability in the tropics further complicates model selection. This underscore the need for machine-learning approaches to identify top-ranked GCMs for projecting future temperature changes in Sarawak. Climatic Research Unit (CRU) datasets were used as observational references, and machine learning-based ranking methods, including compromise programming (CP), entropy gain (EG), gain ratio (GR), random forest (RF), and symmetrical uncertainty (SU), were employed to assess and rank GCM performance. Group decision-making (GDM) and an envelope approach were then applied to identify representative GCMs, followed by the development of a RF-based ensemble for robust projection, featuring IPSL-CM5A-MR, IPSL-CM5A-LR, FIO-ESM, HadGEM2-AO, HadGEM2-ES, MIROC-ESM-CHEM, and GISS-E2-R as key contributors for different temperature indices. The results show that the successive Coupled Model Intercomparison Project Phase 6 (CMIP6) models, such as HadGEM3-GC31-LL, HadGEM3-GC31-MM, and FIO-ESM2-0, demonstrate better alignment with observed records compared to CMIP5, with notable improvements in reducing biases and capturing temporal variability. Ensemble projections reveal consistent warming across Sarawak, with CMIP6 generally indicating stronger warming trends than CMIP5, especially during the Southwest Monsoon (SWM). Spatially, the warming is uneven, highlighting persistent thermal contrasts across different parts of the region. These findings underscore the importance of systematic model evaluation and ensemble approaches in improving regional climate projections, providing critical guidance for climate adaptation, water resource management, and biodiversity conservation in Sarawak.