Heat waves (HWs) can cause large socioeconomic and environmental impacts. The observed increases in their frequency, intensity and duration are projected to continue with global warming. This review synthesizes the state of knowledge and scientific challenges. It discusses different aspects related to the definition, triggering mechanisms, observed changes and future projections of HWs, as well as emerging research lines on subseasonal forecasts and specific types of HWs. We also identify gaps that limit progress and delineate priorities for future research. Overall, the physical drivers of HWs are not well understood, partly due to difficulties in the quantification of their interactions and responses to climate change. Influential factors convey processes at different spatio-temporal scales, from global warming and the large-scale atmospheric circulation to regional and local factors in the affected area and upwind regions. Although some thermodynamic processes have been identified, there is a lack of understanding of dynamical aspects, regional forcings and feedbacks, and their future changes. This hampers the attribution of regional trends and individual events, and reduces the ability to provide accurate forecasts and regional projections. Sustained observational networks, models of diverse complexity, narrative-based methodological approaches and artificial intelligence offer new opportunities toward process-based understanding and interdisciplinary research.

中文翻译：

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热浪：物理理解和科学挑战

热浪 (HW) 会造成巨大的社会经济和环境影响。观察到的频率、强度和持续时间的增加预计将随着全球变暖而继续。本综述综合了知识状况和科学挑战。它讨论了与 HW 的定义、触发机制、观测到的变化和未来预测相关的不同方面，以及关于次季节预报和特定类型 HW 的新兴研究方向。我们还确定了限制进展的差距并描述了未来研究的优先事项。总体而言，HW 的物理驱动因素尚不清楚，部分原因是难以量化它们的相互作用和对气候变化的响应。影响因素传达不同时空尺度的过程，从全球变暖和大范围的大气环流到受影响地区和上风地区的区域和局部因素。尽管已经确定了一些热力学过程，但缺乏对动力学方面、区域强迫和反馈及其未来变化的了解。这阻碍了区域趋势和个别事件的归因，并降低了提供准确预报和区域预测的能力。持续的观察网络、复杂多样的模型、基于叙述的方法论和人工智能为基于过程的理解和跨学科研究提供了新的机会。缺乏对动力方面、区域强迫和反馈及其未来变化的了解。这阻碍了区域趋势和个别事件的归因，并降低了提供准确预报和区域预测的能力。持续的观察网络、复杂多样的模型、基于叙述的方法论和人工智能为基于过程的理解和跨学科研究提供了新的机会。缺乏对动力方面、区域强迫和反馈及其未来变化的了解。这阻碍了区域趋势和个别事件的归因，并降低了提供准确预报和区域预测的能力。持续的观察网络、复杂多样的模型、基于叙述的方法论和人工智能为基于过程的理解和跨学科研究提供了新的机会。