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This study examines future changes in extreme heat stress over East Asia and its sub-regions using wet bulb globe temperature (WBGT) based on the CORDEX East Asia Phase II multiple Regional Climate Model (RCM) simulations performed under Shared Socioeconomic Pathways (SSP) scenarios. Daily maximum WBGTs (WX) are obtained from 3-hourly bias-corrected WBGTs and their future changes in the late 21st century (2081–2100) are analyzed with respect to the current period (1979–2014). Summer mean WX is projected to increase by 3.2 °C (SSP1-2.6: low emission) to 7.6 °C (SSP5-8.5: high emission) over East Asia, dominated by temperature increases. Relative humidity decreases over many regions, slightly offsetting WX increases (up to −6%), while it increases in northeastern and northern China, intensifying WX increases (up to +14%). This humidity-induced WX increase becomes stronger during hottest WX days (summer top 5%) and also under low emission scenarios (up to +33%). For sub-regional projections, extreme heat stress day (EHD) is defined when WX exceeds its 95th percentile in at least 10% of the area. RCMs project on average a 10 times increase of EHD frequency under the SSP5-8.5 scenario. The EHD magnitude, which combines the intensity and area extent of EHD events, is also expected to increase dramatically throughout East Asia, reaching a range of 3.2–3.5 °C∙fraction compared to the current (0.1–0.2 °C∙fraction). Further, EHDs are projected to start earlier and end later, lasting much longer (85–140 days) than the current condition (5–6 days) in the SSP5-8.5 scenario. RCMs exhibit a good agreement in WX and EHD projections with some noticeable differences in in-land sub-regions. Our results indicate that severe heat stress will affect the whole East Asia throughout and beyond the summer season and, in particular, southern sub-regions will be affected by more-intense and longer-lasting extreme heat stress events. © 2023 The Authors

Multimodel ensemble (MME) approach would help modellers to know the advantages of individual global circulation models (GCMs) and to avoid the weaknesses associated with them, and it would help the river basin modellers to make appropriate modelling decisions. The study highlights the river basin-scale development of MME as a convenient way to reduce the parameter and structural uncertainties in the Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs simulations after identifying the best five CMIP6 GCMs based on the rating metric calculations. Furthermore, the performance of the MME was enhanced by integrating three machine learning algorithms (artificial neural network [ANN], random forest [RF], support vector machine [SVM]). Subsequently, comparative assessment depicted the improved performance in MME-integrated ML algorithms compared to simple arithmetic mean (SAM) in simulating observed precipitation (P), maximum temperature (Tmax), and minimum temperature (Tmin) over the Damodar River basin (DRB), India. The statistical metrics indicate that the SVM and RF methods yielded better results than SAM and ANN methods, thus selected for future projections. The robustness of the MME-RF and MME-SVM approach has also been observed while capturing the spatial pattern as IMD-observed with well representation of climate indices for both wet and dry seasons. Future projections with MME-SVM and MME-RF suggested a possible rise in mean annual P in the range of 1.4–15% and 6.8–39% with an increasing trend in temperature (Tmax, Tmin) under the SSP245 and SSP585 scenarios, respectively. Replicating the spatial pattern of the future climatic variables projections evinced a warmer and drier climate in the southwest part of the DRB for both SSP scenarios during wet and dry season and thence warned a probable drier condition on the southwest part of the DRB in future time slices.