The impacts of warm season heat extremes are dependent on both temperature and humidity, so it is critical to properly model their relationship, including how it may be changing. This presents statistical challenges because the bivariate temperature–humidity (measured here by dew point) distribution is complex and spatially variable. Here, we develop a flexible, semiparametric model based on quantile smoothing splines to summarize the distributional dependence of dew point on temperature, including how the dependence is changing with increasing global mean temperature. Noncrossing constraints enforce both the validity of the modeled distributions and the physical constraint that dew point cannot exceed temperature. The proposed method is first demonstrated with four synthetic, representative case studies. We then apply it to data from 2416 weather stations spanning the globe, with a focus on analyzing dew point trends during hot days. In general, dew point is increasing on both hot, humid and hot, dry days in the tropics and high latitudes, but decreasing in the subtropics, especially on hot, dry days. These changes appear to be mostly explained by changes in the temperature–dew point relationship, rather than by increases in temperature with a fixed temperature–dew point relationship.

中文翻译：

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使用非交叉分位数平滑样条估计观察到的湿度和温度之间关系的变化

暖季极端高温的影响取决于温度和湿度，因此正确模拟它们之间的关系至关重要，包括它可能如何变化。这带来了统计上的挑战，因为双变量温湿度（此处由露点测量）分布复杂且空间可变。在这里，我们开发了一个基于分位数平滑样条的灵活的半参数模型，以总结露点对温度的分布依赖性，包括这种依赖性如何随着全球平均温度的升高而变化。非交叉约束加强了建模分布的有效性和露点不能超过温度的物理约束。所提出的方法首先通过四个合成的、具有代表性的案例研究来证明。然后，我们将其应用于来自全球 2416 个气象站的数据，重点是分析炎热天气下的露点趋势。一般来说，在热带和高纬度地区的炎热、潮湿和炎热干燥的日子里，露点都在增加，但在亚热带，特别是在炎热干燥的日子里，露点会下降。这些变化似乎主要由温度-露点关系的变化来解释，而不是由温度-露点关系固定的温度升高来解释。