Projected Intensification of Moist Heatwaves in the Southern Greater Mekong Subregion Using Hybrid Statistical Downscaling of CMIP6 Models

Moist heatwaves, defined by the concurrence of high maximum air temperature and relative humidity, pose increasing risks to human health in tropical regions, yet their representation in global climate models remains limited by coarse spatial resolution and systematic biases. In this study, a hybrid statistical downscaling method (Bias Correction Constructed Analogues with Quantile mapping, BCCAQ) is applied to 17 CMIP6 models to generate 0.1° gridded daily maximum temperature and relative humidity over the southern Greater Mekong Subregion. The downscaled outputs are evaluated against observations for 1981−2014 and used to assess future changes for the near future (2027−2066) and far future (2067−2100) under SSP1−2.6 and SSP5−8.5. The downscaling substantially improves the representation of mean and seasonal climate conditions (added value up to ≈ 0.98), although biases persist toward the upper quantiles. Future projections indicate a robust and spatially coherent warming-drying signal, with maximum temperature rising by up to 3.49 °C by the late century under SSP5−8.5, which together amplifies moist heatwave characteristics relative to the historical baseline. This intensification follows a clear hierarchy: frequency increases only modestly, duration rises more substantially, and temperature-based accumulated intensity shows the strongest and most rapid amplification, with far-future trends reaching 33.33 °C-days per decade under SSP5−8.5 and model agreement of ≥ 94