The evolving distribution of humidity conditional on temperature and implications for compound heat extremes across China in a warming world
In one sentence. Non-crossing quantile smoothing splines emulate humidity-temperature relations at 297 Chinese stations, projecting humid-heat extremes under 2.0 and 4.5 °C warming.
Abstract
The likelihood of extreme heat occurrence is continuously increasing with global warming. Under high temperatures, humidity may exacerbate the heat impact on humanity. As atmospheric humidity depends on moisture availability and is constrained by air temperature, it is important to project the changes in the distribution of atmospheric humidity conditional on air temperature as the climate continuously warms. Here, a non-crossing quantile smoothing spline is employed to build quantile regression models emulating conditional distributions of dew point (a measure of humidity) on local temperature evolving with escalating global mean surface temperature. By applying these models to 297 weather stations in seven regions in China, the study analyzes historical trends of humid-heat and dry-hot days, and projects their changes under global warming of 2.0°C and 4.5°C. In response to global warming, rising trends of humid-heat extremes, while weakening trends of dry-hot extremes, are observed at most stations in Northeast China. Additionally, results indicate an increasing trend in dry-hot extremes at numerous stations across central China, but a rise in humid-heat extremes over Northwest China and coastal regions. These trends found in the current climate state are projected to intensify under 2.0°C and 4.5°C warming, possibly influenced by the heterogeneous variations in precipitation, soil moisture, and water vapor fluxes. Requiring much lower computational resources than coupled climate models, these quantile regression models can further project compound humidity and temperature extremes in response to different levels of global warming, potentially informing the risk management of compound humid-heat extremes on a local scale. 摘要 本研究利用非交叉分位数平滑样条, 对中国七个气候分区的297个气象站分别建立了分位数回归模型, 模拟露点温度基于局地温度的条件概率密度分布对全球变暖的响应, 并预测了这些分布分别在2.0°C和4.5°C温升情景下的变化. 结果表明, (1) 这些分布对全球变暖的响应存在较大的区域异质性: 东北地区, 西北地区与沿海地区大多数站点呈现出极端湿热事件增加的趋势; 而中国中部地区的多个站点呈现出极端干热事件增加的趋势. (2) 这些趋势预计在2.0°C和4.5°C的温升情景下将进一步加剧.
Labels
- Topic
- Extremes & hazards, Climate analysis & attribution
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Climate projections / centennial
- Domain
- Regional
- Data
- Station / in-situ, CMIP / climate model output
- Status
- Research
- Identifiers
- DOI:10.1016/j.aosl.2025.100596 OpenAlex:W4406901429
- Added
- 2026-10-22
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