We review how machine learning has transformed our ability to model the Earth system, and how we expect recent breakthroughs to benefit end-users in Switzerland in the near future. Drawing from our review, we identify three recommendations. Recommendation 1: Develop Hybrid AI-Physical Models: Emphasize the integration of AI and physical modeling for improved reliability, especially for longer prediction horizons, acknowledging the delicate balance between knowledge-based and data-driven components required for optimal performance. Recommendation 2: Emphasize Robustness in AI Downscaling Approaches, favoring techniques that respect physical laws, preserve inter-variable dependencies and spatial structures, and accurately represent extremes at the local scale. Recommendation 3: Promote Inclusive Model Development: Ensure Earth System Model development is open and accessible to diverse stakeholders, enabling forecasters, the public, and AI/statistics experts to use, develop, and engage with the model and its predictions/projections.

翻译：本文回顾了机器学习如何改变了我们模拟地球系统的能力，并展望近期突破如何在不久的将来惠及瑞士终端用户。基于本综述，我们提出三项建议。建议一：发展混合型人工智能-物理模型——强调将人工智能与物理建模相结合以提高可靠性，特别是在更长预报时限内，需要认识到基于知识与数据驱动组件之间的微妙平衡才能实现最优性能。建议二：强调人工智能降尺度方法中的鲁棒性——优先采用尊重物理规律、保留变量间依赖关系与空间结构、准确表征局地极端现象的技术。建议三：推动包容性模型开发——确保地球系统模型开发向多元利益相关方开放且可获取，使预报员、公众及人工智能/统计学专家能够使用、开发并参与模型及其预测/预估的互动。