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Significant advancements in the development of machine learning (ML) modelsfor weather forecasting have produced remarkable results. State-of-the-artML-based weather forecast models, such as FuXi, have demonstrated superiorstatistical forecast performance in comparison to the high-resolution forecasts(HRES) of the European Centre for Medium-Range Weather Forecasts (ECMWF).However, ML models face a common challenge: as forecast lead times increase,they tend to generate increasingly smooth predictions, leading to anunderestimation of the intensity of extreme weather events. To address thischallenge, we developed the FuXi-Extreme model, which employs a denoisingdiffusion probabilistic model (DDPM) to restore finer-scale details in thesurface forecast data generated by the FuXi model in 5-day forecasts. Anevaluation of extreme total precipitation ($\textrm{TP}$), 10-meter wind speed($\textrm{WS10}$), and 2-meter temperature ($\textrm{T2M}$) illustrates thesuperior performance of FuXi-Extreme over both FuXi and HRES. Moreover, whenevaluating tropical cyclone (TC) forecasts based on International Best TrackArchive for Climate Stewardship (IBTrACS) dataset, both FuXi and FuXi-Extremeshows superior performance in TC track forecasts compared to HRES, but theyshow inferior performance in TC intensity forecasts in comparison to HRES.

FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model