基于深度学习的高分辨率中国区域气候模式模拟器OA

Accurately simulating regional climate over East Asia has become increasingly important for understanding the impacts of global climate change.To overcome the coarse spatial resolution of Global Climate Models(GCMs),this study develops a novel Regional Climate Model Emulator(RCM-Emulator)based on deep learning techniques and conducts high-resolution downscaling experiments over East Asia.The proposed model integrates high-resolution topographic and land-sea mask constraints,and introduces additional inputs of incoming shortwave radiation and surface latent heat flux for temperature and precipitation,respectively,thereby enhancing its sensitivity to energy balance and moisture transport processes.Furthermore,a Bernoulli-Gamma loss function is adopted to address the highly skewed nature of precipitation distributions and to improve the representation of extreme rainfall.Results demonstrate that the model can faithfully reconstruct near-surface temperature and precipitation fields in homogeneous experiments using RegCM4 simulations,with the spatial biases remaining minimal and the RMSE values significantly lower than those of bilinear interpolation.When transferred to the ERA5 reanalysis dataset without additional calibration,the emulator successfully reproduces the spatial patterns and temporal variations of the reference data,exhibiting strong cross-dataset generalization.Overall,the proposed RCM-Emulator achieves high physical consistency and computational efficiency,enabling the generation of multi-year regional climate fields within minutes.This approach provides a promising,high-accuracy,and low-cost alternative for regional climate studies,ensemble simulations,and climate risk assessments.