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：东亚夏季降雨的季节预报仍是重大挑战，因其3至6个月领先时间的可预报性受到春季预报障碍、弱大尺度信号及局地非线性对流极端的制约。我们通过CAPES系统应对这一挑战，该系统融合了公里级分辨率的大气-陆地-海洋耦合区域模型与数据驱动的人工智能季节预报系统。在15公里分辨率下，该融合工作流将来自不同启动时间、物理方案和参数扰动的174个数值集合成员与由初始扰动和物理扰动生成的1600个AI集合成员相结合。借助完整的LineShine系统，CAPES在14.6小时内完成了2016至2025年十年间每年1774个集合成员的后报，将ECMWF的平均预报评分从71.8提升至75.9，显著增强了业务预报能力。1公里配置进一步实现了精细尺度台风模拟，并在一周时间尺度上验证了公里级融合集合预报的可行性。