Data-driven forecasts of extreme weather in East Asia: feasibility of operational use

Accurate medium-range weather forecasts are essential for disaster preparedness and resource management. They are traditionally achieved by numerical weather prediction (NWP) models, but such models are costly to run operationally. Recent advances in machine-learning-based weather prediction (MLWP) offer a promising alternative to NWP for improving forecast efficiency. This study evaluates the prediction skills of four state-of-the-art MLWP models—FengWu, FuXi, GraphCast, and PanguWeather—and their ensemble mean (MLWP-EM), initialized with operational analysis from Korea Integrated Model (KIM) as well as ERA5 reanalysis. An emphasis is placed on forecasts of extreme events over East Asia—such as heavy rainfall, typhoons, heat waves, and cold spells in 2023—up to 10 days ahead. When initialized with operational KIM analysis, MLWP forecasts exhibit performance comparable to, or better than…