CMIP6-driven 10 km super-resolution daily climate projections with PET estimates in China

Abstract

Abstract Global warming has intensified extreme weather events, posing challenges to regional climate and hydro-ecological systems. To address the low-resolution limitations of current multi-climate variables and potential evapotranspiration (PET), this study develops a super-resolution fusion framework based on deep residual attention mechanisms, establishing China’s 10-km resolution multi-model-multi-scenario high-resolution climate and PET dataset (SRCPCN10). The Residual Channel Attention Network (RCAN) demonstrates exceptional downscaling performance for temperature, radiation, and pressure (R2/KGE > 0.99), while precipitation exhibits significantly lower accuracy (R2 = 0.897) due to spatial discontinuity. The findings reveal distinct emission-gradient responses in China’s future climate variables under SSP scenarios, with temperature, radiation, and precipitation increases escalating alongside radiative forcing intensification. The comparison of annual mean differences between original CMIP6 and downscaled data showed excellent agreement, with most climate indices differing by less than 1%. This work overcomes traditional limitations, providing kilometer-scale multivariate data for watershed hydrological modeling, agricultural climate risk assessment, and carbon-neutral pathway optimization, enhancing the precision of regional adaptation strategies.