Improving forecasts of extreme rainfall induced by landfalling typhoon Bebinca (2024): Evaluating Fuxi, Pangu and Fengwu AI-driven WRF simulations

Accurately forecasting extreme precipitation remains a longstanding challenge in numerical weather prediction (NWP). Recently, data-driven Artificial Intelligence (AI) models have shown promise in improving global weather forecast accuracy, but their potential to enhance moso-scale precipitation forecasts has not been fully explored. This study evaluates the effectiveness of using forecasts from three AI models (Fuxi, Pangu, and Fengwu) compared with those from the traditional Global Forecast System (GFS) to initialize the Weather Research and Forecasting (WRF) model for simulating the extreme rainfall associated with landfalling Typhoon Bebinca (2024), the strongest typhoon to make landfall in Shanghai since 1949. A total of twenty WRF experiments were conducted across multiple initialization times, enabling a systematic and homogenized comparison of forecast performance. Results show that forecasts from the Fuxi and Pangu models provided more reliable and stable initial conditions, leading to improved predictions of typhoon track and extreme precipitation, particularly at longer lead times. Among the three AI models, Fengwu-driven simulations yielded the lowest track errors and demonstrated superior skill at shorter lead times (within 72 hours). Further physical diagnosis revealed that AI-driven WRF simulations produced more realistic thermodynamic structures, including stronger frontogenesis and enhanced convective organization, which contributed to improved rainfall forecasts. These findings underscore that high-quality large-scale initial fields from AI models not only improve the forecasts of synoptic-scale features such as typhoon track and intensity but also exert critical influence on the location and intensity of precipitation associated with mesoscale convective systems.