Seasonal forecasting of summer rainfall in East Asia remains a grand challenge, as predictability at 3 to 6 month lead times is constrained by the spring predictability barrier, weak large-scale signals, and localized nonlinear convective extremes. We address this challenge with CAPES, which integrates a kilometer-resolution coupled regional model with atmosphere, land, and ocean components and a data-driven AI seasonal forecasting system. At 15 km resolution, the fused workflow combines 174 numerical members from varying start times, physics schemes, and parameter perturbations with 1,600 AI members generated from initial and physical perturbations. Using the full LineShine system, CAPES completes ten annual 1,774-member hindcasts for 2016 to 2025 within 14.6 hours, improving the mean prediction score from ECMWF's 71.8 to 75.9 and delivering a major gain in operational forecasting capability. The 1-km configuration further enables fine-scale typhoon simulation and establishes the feasibility of kilometer-scale fused ensemble forecasting on a one-week timescale.

翻译：东亚夏季降水的季节性预报仍是一个重大挑战，由于春季可预报性屏障、微弱的大尺度信号以及局地非线性对流极端事件，3至6个月超前时间的可预测性受到限制。我们通过CAPES应对这一挑战，该方案集成了包含大气、陆地和海洋分量的公里尺度耦合区域模型与数据驱动的人工智能季节性预报系统。在15公里分辨率下，融合工作流程将来自不同起始时间、物理方案和参数扰动的174个数值集合成员与1600个由初始扰动和物理扰动生成的人工智能集合成员相结合。利用完整的LineShine系统，CAPES可在14.6小时内完成2016至2025年十个年度共1774个成员的批量后报，将平均预报评分从ECMWF的71.8提升至75.9，显著提升了业务预报能力。1公里配置进一步实现了精细尺度台风模拟，并确立了在一周时间尺度上进行公里尺度融合集合预报的可行性。