Advances and Perspectives in Atmospheric Environment Modeling in China

China’s atmospheric environment modeling has advanced rapidly in response to intensifying air pollution challenges, emerging scientific needs, and growing international engagement. This review synthesizes advances across the historical evolution of model systems, key innovations in mechanisms and technologies, and emerging strategic directions. We trace the development from early offline models to fully coupled meteorology–chemistry systems, culminating in high-resolution, multi-pollutant platforms increasingly integrated with artificial intelligence. These models have improved the representation of key processes such as heterogeneous chemistry, secondary aerosol formation, and ozone photochemistry, and have enhanced forecasting capacity through ensemble approaches, data assimilation, and decision-support applications. However, significant challenges remain, including the incomplete simulation of multiphase and feedback processes under compound extremes, limited computational scalability for high-resolution and ensemble use, and fragmented integration of multi-source observations. To address these challenges, this review highlights four priorities: (1) incorporate machine learning into mechanistic modeling; (2) advance open-source and internationally aligned platforms; (3) develop flexible numerical schemes for multi-scale coupling, and (4) embed atmospheric chemistry into Earth system models. China’s experience illustrates not only a national transformation from model adaptation to innovation but also provides transferable insights for the global modeling community.