Chinese AgroMeteorological Model CAMM 2.0–3.0: System Framework, Mechanistic Innovations, and Operational Applications

As climate change intensifies and food security concerns grow, there is an urgent need for advanced agricultural models that can simulate physiological processes precisely and assess risks dynamically. To address the limitations of existing models in China’s complex and diverse cropping systems, this paper presents the Chinese AgroMeteorological Model (CAMM) and describes its systematic development from version 2.0 to the fully operational version 3.0. CAMM 3.0 constitutes an integrated “model–assimilation–platform” trinity. Its core innovations include a dynamic temperature-response phenology model, a coupled photosynthesis–stomatal conductance module, and a novel nonlinear water stress model that spans drought to waterlogging. It mechanistically integrates key regional disaster modules, including freeze injury in winter wheat and heat damage in rice. A dynamic dry matter partitioning framework, governed by development stage, environment, and source–sink balance, enables transparent analysis of yield formation. Supported by a multi-source remote sensing assimilation system, a dynamic parameter regionalization scheme, and seamless meteorological drivers, CAMM 3.0 operates automatically on the national “Tianqing” cloud platform. It generates standardized 5-km resolution products for crop monitoring, stress assessment, and yield forecasting across China. Under independent validation against the WOrld FOod STudies (WOFOST) benchmark, CAMM reduces yield simulation root mean square error (RMSE) by 12.8% and mean relative error by 2 percentage points; paired overfitting tests show no significant difference between dynamic and fixed parameters (p > 0.8), confirming its robustness. The system achieves over 85% accuracy in short-term disaster monitoring. Operational applications confirm its value in providing quantitative, mechanism-based decision support for disaster assessment and national yield prediction, establishing a new paradigm for process-driven agricultural meteorological services that support climate resilience and food security.