  Skillful subseasonal forecasts are crucial for various sectors of society but
pose a grand scientific challenge. Recently, machine learning based weather
forecasting models outperform the most successful numerical weather predictions
generated by the European Centre for Medium-Range Weather Forecasts (ECMWF),
but have not yet surpassed conventional models at subseasonal timescales. This
paper introduces FuXi Subseasonal-to-Seasonal (FuXi-S2S), a machine learning
model that provides global daily mean forecasts up to 42 days, encompassing
five upper-air atmospheric variables at 13 pressure levels and 11 surface
variables. FuXi-S2S, trained on 72 years of daily statistics from ECMWF ERA5
reanalysis data, outperforms the ECMWF's state-of-the-art
Subseasonal-to-Seasonal model in ensemble mean and ensemble forecasts for total
precipitation and outgoing longwave radiation, notably enhancing global
precipitation forecast. The improved performance of FuXi-S2S can be primarily
attributed to its superior capability to capture forecast uncertainty and
accurately predict the Madden-Julian Oscillation (MJO), extending the skillful
MJO prediction from 30 days to 36 days. Moreover, FuXi-S2S not only captures
realistic teleconnections associated with the MJO, but also emerges as a
valuable tool for discovering precursor signals, offering researchers insights
and potentially establishing a new paradigm in Earth system science research.
