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

Fuyao Zhang,

Xiubin Li, Xue Wang

et al.

Scientific Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: April 30, 2025

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 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 PET dataset (SRCPCN10). The Residual Channel Attention Network (RCAN) demonstrates exceptional downscaling performance for temperature, radiation, pressure (R2/KGE > 0.99), while precipitation exhibits significantly lower accuracy (R2 = 0.897) due spatial discontinuity. findings reveal distinct emission-gradient responses in future under SSP scenarios, with increases escalating alongside radiative forcing intensification. comparison annual mean differences between original CMIP6 downscaled data showed excellent agreement, most indices differing by less than 1%. This work overcomes traditional limitations, providing kilometer-scale multivariate watershed hydrological modeling, agricultural risk assessment, carbon-neutral pathway optimization, enhancing precision adaptation strategies.

Language: Английский

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

Fuyao Zhang,

Xiubin Li, Xue Wang

et al.

Scientific Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: April 30, 2025

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 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 PET dataset (SRCPCN10). The Residual Channel Attention Network (RCAN) demonstrates exceptional downscaling performance for temperature, radiation, pressure (R2/KGE > 0.99), while precipitation exhibits significantly lower accuracy (R2 = 0.897) due spatial discontinuity. findings reveal distinct emission-gradient responses in future under SSP scenarios, with increases escalating alongside radiative forcing intensification. comparison annual mean differences between original CMIP6 downscaled data showed excellent agreement, most indices differing by less than 1%. This work overcomes traditional limitations, providing kilometer-scale multivariate watershed hydrological modeling, agricultural risk assessment, carbon-neutral pathway optimization, enhancing precision adaptation strategies.

Language: Английский

Citations

0