Climate modelling and analysis are facing new demands to enhance projections and climate information. Here we argue that now is the time to push the frontiers of machine learning beyond state-of-the-art approaches, not only by developing machine-learning-based Earth system models with greater fidelity, but also by providing new capabilities through emulators for extreme event projections with large ensembles, enhanced detection and attribution methods for extreme events, and advanced climate model analysis and benchmarking. Utilizing this potential requires key machine learning challenges to be addressed, in particular generalization, uncertainty quantification, explainable artificial intelligence and causality. This interdisciplinary effort requires bringing together machine learning and climate scientists, while also leveraging the private sector, to accelerate progress towards actionable climate science.

气候建模和分析面临着加强预测和气候信息的新要求。在这里，我们认为现在是时候推动机器学习的前沿超越最先进的方法了，不仅要开发基于机器学习的更保真度的地球系统模型，还要通过模拟器提供新的功能大型集合的极端事件预测、增强的极端事件检测和归因方法以及先进的气候模型分析和基准测试。利用这种潜力需要解决关键的机器学习挑战，特别是泛化、不确定性量化、可解释的人工智能和因果关系。这种跨学科的努力需要将机器学习和气候科学家聚集在一起，同时也利用私营部门，以加快实现可操作的气候科学的进展。