Global warming is exacerbating weather, and climate extremes events and is projected to aggravate multi-sectorial risks. A multiplicity of climate hazards will be involved, triggering cumulative and interactive impacts on a variety of natural and human systems.

An improved understanding of risk interactions and dynamics is required to support decision makers in their ability to better manage current and future climate change risks. To face this issue, the research community has been starting to test new methodological approaches and tools, including the application of Machine Learning (ML) leveraging the potential of the large availability and variety of spatio-temporal big data for environmental applications. Given the increasing attention on the application of ML methods to Climate Change Risk Assessment (CCRA), this review mapped out the state of art and potential of these methods to this field of research. Scientometric and systematic analysis were jointly applied providing an in-depth review of publications across the 2000–2020 timeframe. The resulting output from the analysis showed that a huge variety of ML algorithms have been already applied within CCRA, among them, the most recurrent are Decision Tree, Random Forest, and Artificial Neural Network. These algorithms are often applied in an ensemble or hybridized way to analyze most of all floods and landslides risk events. Moreover, the application of ML to deal with remote sensing data is consistent and effective across reviewed CCRA applications, allowing the identification and classification of targets and the detection of environmental and structural features. On the contrary concerning future climate change scenarios, literature seems not to be very widespread into scientific production, compared to studies evaluating risks under current conditions. The same lack can be noted also for the assessment of cascading and compound hazards and risks, since these concepts are recently emerging in CCRA literature but not yet in combination with ML-based applications.

全球变暖正在加剧天气和气候极端事件，预计会加剧多部门风险。将涉及多种气候危害，引发对各种自然和人类系统的累积和互动影响。

需要更好地了解风险相互作用和动态，以支持决策者更好地管理当前和未来的气候变化风险。为了面对这个问题，研究界已经开始测试新的方法论方法和工具，包括机器学习 (ML) 的应用，利用大量可用性和各种时空大数据在环境应用中的潜力。鉴于 ML 方法在气候变化风险评估 (CCRA) 中的应用受到越来越多的关注，本综述将这些方法的最新技术和潜力映射到该研究领域。科学计量学和系统分析被联合应用，提供了对 2000-2020 年时间范围内出版物的深入审查。分析结果表明，CCRA 中已经应用了大量 ML 算法，其中最经常出现的是决策树、随机森林和人工神经网络。这些算法通常以集成或混合方式应用，以分析大多数洪水和滑坡风险事件。此外，ML 处理遥感数据的应用在已审查的 CCRA 应用程序中是一致且有效的，允许识别和分类目标以及检测环境和结构特征。相反，关于未来的气候变化情景，与评估当前条件下风险的研究相比，文献在科学生产中似乎并不广泛。