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

湿热对人体健康构成严重威胁，会降低人体通过出汗降温的能力。气候变化对人类的影响将会增加，特别是在热带和亚热带的热点地区，例如赤道非洲和印度次大陆，这些地区人口稠密，已经非常炎热和潮湿。虽然对干球极端温度的研究越来越多，但对极端湿热天气的气象驱动因素，特别是水分输送、降雨和地球表面水分蒸发的作用，了解非常有限。在极端湿热的情况下，天气和气候模型表示这些过程并产生准确的天气预报和气候预测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加剧的极端气候的影响，但这一点仍然存在。我们将在一系列尺度上量化不同湿热驱动因素的相对重要性，从局部地表通量到天气形势，再到热带气候变率的全球尺度模式。我们将绘制出在当前气候和未来可能的气候下，在影响更大地区的更广泛的事件中，有更高风险经历最高湿热的特定地点(在村庄或城镇规模)。我们将对可能出现的复合气候极端情况进行量化：湿热、暴雨和洪水并存。这些结果将为改进湿热事件的预测提供基础知识，为天气和气候变化时间尺度上的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.