| 15 Aug 2025
Precipitation-temperature scaling: current challenges and proposed methodological strategies
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.
Let me start by stating the manuscript is well written and I believe worthy of publication. However, I am concerned that the novelty has been misrepresented. The main issue here is the authors provide 3 shortcomings they have overcome building on Zhang et al (2017). I do not agree with this statement as I believe each individual shortcoming has been addressed in previous literature. Here the novelty lies in combining the approaches from three different manuscripts. I believe this combining of methods is a worthy contribution and an important one. I have two major comments which I hope the authors can address.
Major comment 1.
I strongly believe that data for P-T scaling should not be pooled without standardising – and agree with the authors, but this was demonstrated in Visser et al (2022) and Molnar et al (2015). The authors have cited these papers in their manuscript and to their own admission at line 379 they state their work bears a strong resemblance to Visser et al (2022) and Molnar et al (2015) so why not make this point in the introduction that they build on these authors work?
Further, Figure 2: A standardisation was already proposed in Visser et al (2022) and I quote in their introduction “We introduce standardized pooling…”. This should be acknowledged here.
Figure 3 and 4: The issue that bins at the extremes have less events, and some binning techniques don’t consider independence were both points made in Wasko and Sharma (2014) and hence quantile regression using independent events was proposed. This point also relates to Line 406. This should be acknowledged here.
Figure 5: The use of a monthly (or seasonal) temperature was proposed by Zhang et al (2017). This should be acknowledged here.
In sum, while the justification of the proposed methodology presented in this manuscript is much more elaborate than previous manuscripts (and hence I am a proponent of it being published) the framing needs to change I believe to duly pay respect to the previous research. The method proposed here is more a combination of methods proposed by Visser et al (2022), Wasko et al (2014), and Zhang et al (2017) and the introduction and conclusion should be restructured accordingly.
Major comment 2.
It is odd that the authors choose 7% per degree as their truth when calculating the skill, when by their own admission in Figure 12 the scaling is not aligned with CC? In some way this should addressed, with at least more focus on the actual scaling rates. The reason is – these are empirical relationships, without a “truth”.
Minor comments:
Title: The title suggests a review and noting that some of the current challenges have been resolved the title could be amended.
Line 1-2: Does sub-daily rainfall scale at 7%? There is now much review/meta-analysis work showing it is likely higher? e.g. Fowler et al (2021); Wasko et al (2024). The IPCC reports also point to higher than 7% scaling for sub-daily rainfall.
Line 60 onwards: The point of pooling resulting in “incorrect” scaling was well made in Molnar et al (2015) and has been made in papers by Berg and Haerter – making the point that a lot of this has to do with different storm types, but this was never mentioned here?
Line 113: “incorrect” is a strong word when we don’t know the truth, scaling’s are correlations and they’re all true in some way regardless of the method.
Figure 8 nicely presents that the pooling of standardized data works, but Figure 8d also shows that monthly data can be safely pooled after standardization and the performance is similar (Column 1 vs Column 4) – could this point me made in the text?
References:
Fowler, H.J., Lenderink, G., Prein, A.F., Westra, S., Allan, R.P., Ban, N., Barbero, R., Berg, P., Blenkinsop, S., Do, H.X., Guerreiro, S., Haerter, J.O., Kendon, E.J., Lewis, E., Schaer, C., Sharma, A., Villarini, G., Wasko, C., Zhang, X., 2021. Anthropogenic intensification of short-duration rainfall extremes. Nature Reviews Earth & Environment 2, 107–122. https://doi.org/10.1038/s43017-020-00128-6
Molnar, P., Fatichi, S., Gaál, L., Szolgay, J., Burlando, P., 2015. Storm type effects on super Clausius–Clapeyron scaling of intense rainstorm properties with air temperature. Hydrology and Earth System Sciences 19, 1753–1766. https://doi.org/10.5194/hess-19-1753-2015
Visser, J.B., Wasko, C., Sharma, A., Nathan, R., 2021. Eliminating the “hook” in Precipitation-Temperature Scaling. Journal of Climate 34, 9535–9549. https://doi.org/10.1175/JCLI-D-21-0292.1
Wasko, C., Sharma, A., 2014. Quantile regression for investigating scaling of extreme precipitation with temperature. Water Resources Research 50, 3608–3614. https://doi.org/10.1002/2013WR015194
Wasko, C., Westra, S., Nathan, R., Pepler, A., Raupach, T.H., Dowdy, A., Johnson, F., Ho, M., McInnes, K.L., Jakob, D., Evans, J., Villarini, G., Fowler, H.J., 2024. A systematic review of climate change science relevant to Australian design flood estimation. Hydrology and Earth System Sciences 28, 1251–1285. https://doi.org/10.5194/hess-28-1251-2024
Zhang, X., Zwiers, F.W., Li, G., Wan, H., Cannon, A.J., 2017. Complexity in estimating past and future extreme short-duration rainfall. Nature Geoscience 10, 255–259. https://doi.org/10.1038/ngeo2911