Humid heat extremes in the Global (sub)Tropics (H2X)

湿热对人体健康是一个严重的威胁，会降低人体通过出汗来降温的能力。气候变化对人类的影响将会增加，特别是在热带和亚热带“热点”地区，如赤道非洲和印度次大陆，这些地区人口众多，已经非常炎热潮湿。虽然对极端干球温度的研究越来越多，但对极端湿热的气象驱动因素的了解非常有限，特别是对水分输送、降雨和地球表面水分蒸发的作用。在极端湿热情况下，天气和气候模式表示这些过程并产生准确天气预报和气候预估的能力在很大程度上是无法量化的。尽管迫切需要适应和减轻全球日益增加的极端气候的影响。我们将量化不同湿热驱动因素在一系列尺度上的相对重要性，从局部地表通量到天气天气模式，再到热带气候变率的全球尺度模式。我们将绘制特定地点（以村庄或城镇为尺度）的地图，这些地点在影响更大区域的更广泛的事件中，在当前气候和可能的未来气候下，经历湿热最高最大值的风险增加。我们将量化一种可能出现的复合极端气候：湿热、暴雨和洪水的共同出现。研究结果将为改进湿热事件的预测提供基础知识，为跨天气和气候变化时间尺度的早期预警系统开发和决策提供信息。

Humid heat is a serious risk to human health, reducing the body's ability to cool itself through sweating. The impact on humans will increase under climate change, particularly in tropical and sub-tropical 'hot spots', such as equatorial Africa and the Indian subcontinent, which are highly populated, and already very hot and humid. Whilst there is a growing body of research on dry-bulb temperature extremes, there is very limited understanding of the meteorological drivers of humid heat extremes, particularly the role of moisture transport, rainfall, and evaporation of moisture from the Earth's surface. In the context of humid heat extremes, the ability of weather and climate models to represent these processes and produce accurate weather forecasts and climate projections is largely unquantified. This is despite an urgency to adapt to and mitigate the impacts of globally increasing climate extremes. We will quantify the relative importance of different humid heat drivers on a cascade of scales, from local surface fluxes, to synoptic weather patterns, to the global-scale modes of tropical climate variability. We will map the specific locations (at the village or town scale) that have an increased risk of experiencing the highest maxima in humid heat during more widespread events that affect a larger region, under both current climate and possible future climates. We will quantify a possible emerging compound climate extreme: the co-occurrence of humid heat, heavy rainfall and flooding. The results will provide underpinning knowledge to improve the prediction of humid heat events, informing Early Warning System development and decision making across weather and climate change time-scales.