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.

翻译：熟练的次季节预报对社会各部门至关重要，但也是一个重大的科学挑战。最近，基于机器学习的天气预报模型在性能上超越了欧洲中期天气预报中心（ECMWF）生成的最成功的数值天气预报，但在次季节时间尺度上尚未超越传统模型。本文介绍了FuXi次季节至季节（FuXi-S2S）模型，这是一种机器学习模型，可提供长达42天的全球日平均预报，涵盖13个气压层的5个高空大气变量和11个地表变量。FuXi-S2S基于ECMWF ERA5再分析数据72年的日统计数据训练而成，在总降水和向外长波辐射的集合平均和集合预报方面，其性能超越了ECMWF最先进的次季节至季节模型，显著提升了全球降水预报能力。FuXi-S2S性能的提升主要归功于其卓越的捕捉预报不确定性的能力以及对马登-朱利安振荡（MJO）的准确预测，将MJO的有效预测时间从30天延长至36天。此外，FuXi-S2S不仅能够捕捉与MJO相关的真实遥相关，还成为一种发现前兆信号的有价值工具，为研究人员提供了新的见解，并可能为地球系统科学研究建立新的范式。