Intelligent Shanghai Typhoon Model (ISTM): A Diffusion‐Based Downscaling Emulator From AIFS to Kilometer‐Scale Regional Reanalysis for Typhoon Forecasts

Abstract Artificial‐intelligence weather prediction models have recently surpassed numerical models in large‐scale skill, but they still systematically underestimate typhoon intensity due to their reliance on coarse‐resolution training data from ERA5. To overcome this limitation, we constructed a bespoke 9‐km high‐resolution typhoon reanalysis (HiRes) by dynamically downscaling ERA5 with the operational Shanghai Typhoon Model (SHTM). The evaluation shows that HiRes has a clear advantage over ERA5 in aspects such as surface wind and land precipitation. HiRes inherently combines a large‐scale deterministic background with small‐scale residual corrections, motivating a learning framework that mirrors this structure. We therefore introduce the Intelligent Shanghai Typhoon Model (ISTM), a regression‐residual diffusion emulator that first predicts the conditional mean fields and then stochastically reconstructs the residuals with a conditional diffusion model. Trained on HiRes, ISTM substantially improves wind and precipitation intensity and organization compared with ERA5 and deterministic U‐Net baselines. After fine‐tuning, ISTM can downscale AIFS forecast fields to produce SHTM‐consistent, physically coherent fields; while maintaining track errors comparable to AIFS, it reduces the 72‐hr mean typhoon intensity forecast error by about 39.5% relative to AIFS.