Skillful subseasonal forecasts beyond 2 weeks are crucial for a wide range of
applications across various sectors of society. Recently, state-of-the-art
machine learning based weather forecasting models have made significant
advancements, outperforming the high-resolution forecast (HRES) from the
European Centre for Medium-Range Weather Forecasts (ECMWF). However, the full
potential of machine learning models in subseasonal forecasts has yet to be
fully explored. In this study, we introduce FuXi Subseasonal-to-Seasonal
(FuXi-S2S), a machine learning based subseasonal forecasting model that
provides global daily mean forecasts up to 42 days, covering 5 upper-air
atmospheric variables at 13 pressure levels and 11 surface variables. FuXi-S2S
integrates an enhanced FuXi base model with a perturbation module for
flow-dependent perturbations in hidden features, and incorporates Perlin noise
to perturb initial conditions. The model is developed using 72 years of daily
statistics from ECMWF ERA5 reanalysis data. When compared to the ECMWF
Subseasonal-to-Seasonal (S2S) reforecasts, the FuXi-S2S forecasts demonstrate
superior deterministic and ensemble forecasts for total precipitation (TP),
outgoing longwave radiation (OLR), and geopotential at 500 hPa (Z500). Although
it shows slightly inferior performance in predicting 2-meter temperature (T2M),
it has clear advantages over land area. Regarding the extreme forecasts,
FuXi-S2S outperforms ECMWF S2S globally for TP. Furthermore, FuXi-S2S forecasts
surpass the ECMWF S2S reforecasts in predicting the Madden Julian Oscillation
(MJO), a key source of subseasonal predictability. They extend the skillful
prediction of MJO from 30 days to 36 days.

超过 2 周的熟练次季节预测对社会各个领域的广泛应用至关重要。本研究介绍了基于机器学习的 FuXi-S2S 次季节预测模型，它以全球每日平均预测为特点，覆盖了 13 个压力层上的 5 个高层大气变量和 11 个地表变量，提供了高达 42 天的预测。与 ECMWF 的次季节预测相比，FuXi-S2S 模型在总降水、出射长波辐射和 500 hPa 高空位势等方面展示出优越的确定性和集合预测，对 Madden Julian Oscillation (MJO) 的预测从 30 天延长到 36 天。