Abstract. Sub-daily to daily extreme precipitation intensities are expected to increase in a warming climate, consistent with the Clausius-Clapeyron (CC) relationship, which predicts a ∼7 % increase in atmospheric moisture-holding capacity per °C of warming. Many studies have benchmarked observed extreme precipitation–temperature (P–T) scaling rates against this theoretical value, finding that global averages align closely with CC, while regional and seasonal estimates often diverge substantially. Significant challenges remain, however, in accurately estimating and interpreting P–T scaling rates, particularly at point scales. In this study, we use observational data from the Upper Colorado River Basin to explore these challenges and propose methodological improvements. Specifically, we compare multiple approaches, including those using raw (non-normalized) and normalized data, to estimate P–T scaling for hourly and daily extreme precipitation. Model performance is assessed using a cross-validation framework. Our results demonstrate that normalizing data, independently for every station and each calendar month, is essential to account for spatial and temporal climatological variability. Without normalization, estimated scaling rates can be inaccurate and misleading.
Switanek et al. (Fri,) studied this question.