Fusing Physics and AI Enhances Accuracy and Diagnostic Capabilities in Air Quality Forecasting

Abstract Accurate air-quality forecasts with diagnostic value are pivotal for emission-control policies and public health, yet chemical transport models (CTMs) are hampered by uncertainties in physicochemical parameterizations, whereas AI-only approaches sacrifice interpretability for predictive power. Here, we introduce ProNet, a coupled forecasting framework that embeds observation-constrained neural correctors directly into the governing equations of CTMs. Rather than treating AI as an external postprocessor or a process-replacing surrogate, ProNet integrates trainable networks into physical simulations to systematically reduce process-level uncertainties, suppress error propagation, and maintain physical constraints of mass transport. Applied to 2022–2023 data across 338 Chinese cities, ProNet consistently outperforms physical CTMs and AI-only baselines, and better generalizes to extreme events beyond the training data. It also surpasses forecaster-adjusted operational forecasts, which often improve upon raw CTM guidance in severe episodes, improving accuracy by 6.1% for PM2.5 and 3.0% for O3. Notably, ProNet provides error bookkeeping, allowing forecasters to quantitatively attribute simulation errors to specific space and time. In-depth analysis of these process errors reveals systematic biases in current CTM parameterizations, providing actionable insights to refine the physical and chemical modules of numerical models. These results highlight the promise of physics-AI coupling for advancing air quality forecasting and deepening our understanding of the complex Earth system.