The likelihood of extreme heat occurrences is continuously increasing with global warming. On hot days, high humidity also plays a critical role in affecting human-health. As the humidity of air depends on the moisture availability and is constrained by the air temperature, it is important to project the changes in distribution of air humidity conditional on air temperature as climate continuous warming. Here a non-crossing quantile smoothing spline is employed to build a statistic model emulating conditional distributions of dewpoint on local temperature evolving with escalating global mean surface temperature. By applying these models to 297 weather stations in seven climate types in China, the study analyzes historical trends of humid-heat and dry-hot days, and projects their changes under global warming of 2.0 or 4.5℃. The results indicate the in most stations of Northeast China, the humidity shows a rising trend in both dry-hot and humid-heat conditions. Besides, intensification of dry-hot extremes is shown at many stations of central China, and enhancement of humid-heat extremes at many stations over the northwest China and areas close to the China coast as climate warming, which is projected to continue and intensify under warming levels of 2.0 and 4.5℃. Analysis reveals that variations of precipitation, soil moisture, and water vapor fluxes may be the pivotal factors influencing the phenomenon. These results could provide quantitative heterogeneous projections for distributions of compound humidity and temperature extremes caused by global warming, which can further inform the mitigation at global level and adaptation at local level.
 

global warming, heat events, conditional distribution of dewpoint, non-crossing quantile smoothing spline model