Accurate subseasonal-to-seasonal (S2S) prediction of extreme events is critical for resource planning and disaster mitigation under accelerating climate change. However, such predictions remain challenging due to complex multi-sphere interactions and intrinsic atmospheric uncertainty. Here we present TianXing-S2S, a multi-sphere coupled probabilistic model for global S2S daily ensemble forecast. TianXing-S2S first encodes diverse multi-sphere predictors into a compact latent space, then employs a diffusion model to generate daily ensemble forecasts. A novel coupling module based on optimal transport (OT) is incorporated in the denoiser to optimize the interactions between atmospheric and multi-sphere boundary conditions. Across key atmospheric variables, TianXing-S2S outperforms both the European Centre for Medium-Range Weather Forecasts (ECMWF) S2S system and FuXi-S2S in 45-day daily-mean ensemble forecasts at 1.5 resolution. Our model achieves skillful subseasonal prediction of extreme events including heat waves and anomalous precipitation, identifying soil moisture as a critical precursor signal. Furthermore, we demonstrate that TianXing-S2S can generate stable rollout forecasts up to 180 days, establishing a robust framework for S2S research in a warming world.

翻译：在气候加速变化的背景下，次季节至季节（S2S）尺度极端事件的精准预测对于资源规划和灾害减缓至关重要。然而，由于复杂的多圈层相互作用及大气内在不确定性，此类预测仍面临巨大挑战。本文提出TianXing-S2S——一个面向全球S2S逐日集合预报的多圈层耦合概率模型。该模型首先将多样化的多圈层预报因子编码至紧凑的潜空间，进而采用扩散模型生成逐日集合预报。在去噪器中创新性地引入了基于最优传输（OT）的耦合模块，以优化大气与多圈层边界条件间的相互作用。在关键大气变量上，TianXing-S2S在1.5°分辨率、45天日均集合预报中，其性能均优于欧洲中期天气预报中心（ECMWF）S2S系统及FuXi-S2S模型。本模型实现了对热浪和异常降水等极端事件的次季节尺度精准预测，并识别出土壤湿度是关键的前兆信号。此外，我们证明TianXing-S2S能够生成长达180天的稳定滚动预报，为变暖背景下的S2S研究建立了稳健的框架。