Global warming is expected to have profound socio-economic and environmental consequences, and one of the key concerns is extreme precipitation. The classic theory indicates that the variation of extreme precipitation will follow the thermodynamically based Clausius-Clapeyron relation (increasing at a rate of approximately 7%/°C). However, the interaction and the seasonal variation of the relationship between extreme precipitation and temperature has not been thoroughly integrated. Here, we use quantile regression and the binning method to process meteorological station data and the reanalysis data from 78 stations on the Tibetan Plateau (TP), and estimate the sensitivity and dependency of extreme precipitation to both surface air temperature and dew point temperature. The results indicate that the majority of the meteorological stations experience a positive scaling between extreme precipitation and surface air temperature, with the median of surface air temperature scaling close to 2.5%/°C. With regard to scaling during individual seasons, 80% of stations possess negative scaling of the 99th percentile of extreme precipitation with temperature in summer, while 63 stations (81% of the total) display positive trends in winter, and the other 19% have negative scaling. A stronger scaling (3.5%/°C) occurs between dewpoint temperature and extreme precipitation. For surface air temperature < −3 °C and > 8 °C, relative humidity decreases with increasing temperature and dewpoint depression increases, so dewpoint temperature increases more slowly than temperature, resulting in the stronger scaling relationship with dewpoint temperature. The depression of dewpoint temperature presents a slight decrease in −3–8 °C interval, but dewpoint temperature increases faster than other intervals, and the scaling relationship of dewpoint temperature is consistently larger than with surface air temperature due to the small increasing rate with dewpoint temperature and increasing extreme precipitation intensity. Our results emphasize that the increasing temperature has clear scaling and seasonal relationship with extreme precipitation on the TP, and the atmospheric humidity variation has an effect on the overall difference in the scaling relationships of extreme precipitation with surface air temperature and dewpoint temperature. The variations of seasonality and the effects of atmospheric humidity for extreme precipitation should also be carefully considered in future research.

预计全球变暖将对社会经济和环境产生深远的影响，其中最主要的关切之一是极端降水。经典理论表明，极端降水的变化将遵循基于热力学的克劳修斯-克拉珀龙关系（以大约7％/°C的速率增加）。然而，极端降水与温度之间的相互作用和季节变化的关系尚未得到完全整合。在这里，我们使用分位数回归和分箱方法来处理青藏高原（TP）上78个气象站的气象站数据和再分析数据，并估计极端降水对地表气温和露点温度的敏感性和依赖性。结果表明，大多数气象站的极端降水量与地表气温之间存在正比例关系，地表气温比例中值接近2.5％/°C。关于单个季节的结垢，夏季有80％的气象站对极端降水的第99个百分位数具有负缩放，而夏季有63个气象站（占总数的81％）在冬季呈正趋势，而其他19％的气象站则呈负趋势。缩放。在露点温度和极端降水之间会出现更强的结垢（3.5％/°C）。对于地表气温<-3°C和> 8°C，相对湿度随温度升高而降低，露点降低量增加，因此，露点温度的升高速度要慢于温度，导致与露点温度的比例关系更强。露点温度的下降在−3–8°C的区间内略有下降，但露点温度的上升速度快于其他区间，并且由于露点的上升速率小，因此露点温度的标度关系始终大于表面空气温度的标度关系温度和极端降水强度的增加。我们的结果强调，温度升高与TP上极端降水的比例关系和季节有明确的关系，大气湿度的变化对极端降水与地面气温和露点温度的比例关系的整体差异有影响。