Modeling the scaling of short‐duration precipitation extremes with temperature

The Clausius-Clapeyron (CC) relation expresses the exponential increase in the moisture-holding capacity of air of approximately 7%/degrees C. Earlier studies show that extreme hourly precipitation increases with daily mean temperature, consistent with the CC relation. Recent studies at specific locations found that for temperatures higher than around 12 degrees C, hourly precipitation extremes scale at rates higher than the CC scaling, a phenomenon that is often referred to as "super-CC scaling." These scalings are typically estimated by collecting rainfall data in temperature bins, followed by a linear fit or a visual inspection of the precipitation quantiles in each bin. In this study, a piecewise linear quantile regression model is presented for a more flexible, and robust estimation of the scaling parameters, and their associated uncertainties. Moreover, we use associated information criteria to prove statistically whether or not a pronounced super-CC scaling exists. The techniques were tested on stochastically simulated data and, when applied to hourly station data across Western Europe and Scandinavia, revealed large uncertainties in the scaling rates. Finally, goodness-of-fit measures indicated that the dew point temperature is a better scaling predictor than temperature.