Seamless forecasting that produces warning information at continuum timescales based on only one system is a long-standing pursuit for weather-climate service. While the rapid advancement of deep learning has induced revolutionary changes in classical forecasting field, current efforts are still focused on building separate AI models for weather and climate forecasts. To explore the seamless forecasting ability based on one AI model, we propose FengWu-Weather to Subseasonal (FengWu-W2S), which builds on the FengWu global weather forecast model and incorporates an ocean-atmosphere-land coupling structure along with a diverse perturbation strategy. FengWu-W2S can generate 6-hourly atmosphere forecasts extending up to 42 days through an autoregressive and seamless manner. Our hindcast results demonstrate that FengWu-W2S reliably predicts atmospheric conditions out to 3-6 weeks ahead, enhancing predictive capabilities for global surface air temperature, precipitation, geopotential height and intraseasonal signals such as the Madden-Julian Oscillation (MJO) and North Atlantic Oscillation (NAO). Moreover, our ablation experiments on forecast error growth from daily to seasonal timescales reveal potential pathways for developing AI-based integrated system for seamless weather-climate forecasting in the future.

翻译：基于单一系统在连续时间尺度上生成预警信息的无缝预报，是天气-气候服务领域长期追求的目标。尽管深度学习的快速发展已为经典预报领域带来了革命性变化，但当前的努力仍集中于为天气和气候预报分别构建独立的人工智能模型。为探索基于单一AI模型的无缝预报能力，我们提出了FengWu-Weather to Subseasonal (FengWu-W2S)模型。该模型基于FengWu全球天气预报模型构建，融合了海洋-大气-陆地耦合结构以及多样化的扰动策略。FengWu-W2S能够以自回归和无缝衔接的方式，生成时间跨度长达42天、时间分辨率为6小时的大气预报。我们的后报结果表明，FengWu-W2S能够可靠地预测未来3至6周的大气状况，提升了对全球地表气温、降水、位势高度以及季节内信号（如马登-朱利安振荡和北大西洋涛动）的预测能力。此外，我们针对从日尺度到季节尺度预报误差增长的消融实验，揭示了未来开发基于人工智能的天气-气候无缝集成预报系统的潜在路径。