Quantifying Atmospheric Radiative Effects over China with a Hybrid Modeling Framework

Accurate quantification of atmospheric factor impacts on surface solar radiation is crucial for solar energy resource assessment and climate change research but remains challenging due to the computational cost of radiative transfer models and limitations in observational data. This study employs a hybrid algorithm framework based on radiative transfer coupled with machine learning models to systematically analyze the impact mechanisms and spatiotemporal evolution characteristics of aerosols， clouds， water vapor， and ozone on surface solar radiation over China. Results reveal a significant surface brightening trend， with the aerosol direct radiative effect declining at a rate of -0.13% yr^-1. However， this brightening is partially offset by a strengthening of cloud attenuation effects and an intensifying water vapor radiative effect， which showed an increasing trend in 82% of the studied regions. Clouds remain the dominant modulator of surface solar radiation （70%-78% relative contribution）. In parts of Xinjiang， Northeast China， and Tibet， water vapor-radiation interaction relative contributions to surface solar radiation changes approach or match aerosol-radiation interaction relative contributions to surface solar radiation changes. These findings reveal the complex role of atmospheric components in modulating surface solar radiation， providing essential reference for China’s climate change research.