A data-to-forecast machine learning system for global weather

Xiuyu Sun et al. (2025). A data-to-forecast machine learning system for global weather. Nature Communications, https://doi.org/10.1038/s41467-025-62024-1

• FuXi Weather is introduced as a groundbreaking end-to-end machine learning system for global weather forecasting. It autonomously performs data assimilation and forecasting in a 6-hour cycle, directly processing raw multi-satellite observations, and notably, it is the first such system to demonstrate continuous cycling operation over a full one-year period.

• The system exhibits superior forecast accuracy in observation-sparse regions, outperforming traditional high-resolution forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF HRES) beyond day one in areas like central Africa and northern South America, despite utilizing substantially fewer observations

.• Globally, FuXi Weather delivers comparable 10-day forecast performance to ECMWF HRES, generating reliable forecasts at a 0.25° resolution and extending the skillful lead times for a number of key meteorological variables

.• FuXi Weather offers a cost-effective and physically consistent alternative to traditional Numerical Weather Prediction (NWP) systems. Its computational efficiency and reduced complexity are valuable for improving operational forecasts and enhancing climate resilience in regions with limited land-based observational infrastructure

.• This development challenges the prevailing view that standalone machine learning-based weather forecasting systems are not viable for operational use, demonstrating a significant step forward in the application of AI to real-world weather prediction.