Extreme events such as heat waves and cold spells, droughts, heavy rain, and storms are particularly challenging to predict accurately due to their rarity and chaotic nature, and because of model limitations. However, recent studies have shown that there might be systemic predictability that is not being leveraged, whose exploitation could meet the need for reliable predictions of aggregated extreme weather measures on timescales from weeks to decades ahead. Recently, numerous studies have been devoted to the use of artificial intelligence (AI) to study predictability and make climate predictions. AI techniques have shown great potential to improve the prediction of extreme events and uncover their links to large‐scale and local drivers. Machine and deep learning have been explored to enhance prediction, while causal discovery and explainable AI have been tested to improve our understanding of the processes underlying predictability. Hybrid predictions combining AI, which can reveal unknown spatiotemporal connections from data, with climate models that provide the theoretical foundation and interpretability of the physical world, have shown that improving prediction skills of extremes on climate‐relevant timescales is possible. However, numerous challenges persist in various aspects, including data curation, model uncertainty, generalizability, reproducibility of methods, and workflows. This review aims at overviewing achievements and challenges in the use of AI techniques to improve the prediction of extremes at the subseasonal to decadal timescale. A few best practices are identified to increase trust in these novel techniques, and future perspectives are envisaged for further scientific development.This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models The Social Status of Climate Change Knowledge > Climate Science and Decision Making

中文翻译：

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用于极端气候预测的人工智能：最新技术、挑战和未来前景


热浪和寒潮、干旱、暴雨和风暴等极端事件由于其罕见性和混乱性以及模型的局限性而特别难以准确预测。然而，最近的研究表明，可能存在未被利用的系统可预测性，其利用可以满足对未来几周到几十年的时间尺度上汇总的极端天气措施进行可靠预测的需求。最近，大量研究致力于使用人工智能（AI）来研究可预测性并进行气候预测。人工智能技术在改善极端事件的预测并揭示其与大规模和当地驱动因素的联系方面表现出了巨大的潜力。人们探索了机器和深度学习来增强预测，同时测试了因果发现和可解释的人工智能，以提高我们对可预测性背后过程的理解。将人工智能（可以从数据中揭示未知的时空联系）与气候模型（提供物理世界的理论基础和可解释性）相结合的混合预测表明，提高与气候相关的时间尺度上的极端事件的预测技能是可能的。然而，在各个方面仍然存在许多挑战，包括数据管理、模型不确定性、通用性、方法的可重复性和工作流程。本综述旨在概述使用人工智能技术改进次季节到十年时间尺度的极端事件预测方面的成就和挑战。确定了一些最佳实践，以增加对这些新技术的信任，并展望了进一步科学发展的未来前景。本文分类如下： 气候模型和建模 > 通过模型生成知识 气候变化知识的社会地位 > 气候科学与决策