| 12 Jan 2026
Benefits of the simplified MEV for analyzing hourly precipitation extremes in a changing climate
Abstract. Predicting the likelihood of extreme hourly rainfall events is crucial in mitigating risks associated with flash floods and related hazards. Previous research shows that, for limited sample sizes, the simplified Metastatistical Extreme Value (sMEV) distribution can significantly reduce the associated uncertainty in rainfall return levels compared to the more commonly used General Extreme Value (GEV) distribution. Recent research also highlights the possibility to analyze the effects of climate change using the non-stationary versions of both distributions. Thus, we evaluate the performance of the sMEV and GEV distributions for hourly precipitation obtained from a convection-permitting regional climate model. The global climate model MIROC5 is employed to drive the regional climate model COSMO over the greater Germany area for historical, near-future and far-future periods. To our knowledge, this is the first application of the sMEV distribution to time series from a convection-permitting-model. The results show that the sMEV outperforms the GEV in terms of uncertainty across almost all return periods regardless of the length of observational records. In addition, there is a north-south gradient in the return level difference, the uncertainty difference and crucially the adequacy of the sMEV left-censoring threshold. Investigating non-stationary versions of the sMEV and GEV shows that the non-stationary sMEV is more suitable to describing the change in return levels under climate change. However, both non-stationary versions analyzed lack complexity and should be used carefully when projecting future rainfall extremes.
This manuscript applies the GEV and SMEV statistical distributions to estimate extreme rainfall return periods using data from a high-resolution climate model that allows for convection. The article is relatively well written, but I find many sentences and sections to be very succinct and in need of further development. In terms of objectives, they are not clear to me. Is the methodological objective to compare distributions, in which case I think the method is not appropriate, or is it to compare the projections produced by the two frequency models?
I strongly recommend a major revision of this manuscript to clarify the methods used and, above all, to specify the objective of this work. If the methodological objective is to say that the SMEV method is more robust than the GEV approach, there is nothing new here, as this has been demonstrated in previous work. On the other hand, I think it would be interesting to present future projections for hourly rainfall in Germany using these two approaches. If this objective is retained, a more comprehensive literature review is needed to explain what projections are currently available in Germany on hourly extremes and how the results of this study either support them or produce new results.
Abstract: “. Thus, we evaluate the performance of the sMEV5 and GEV distributions for hourly precipitation obtained from a convection-permitting regional climate mode” => In fact, this is not really what is done here to evaluate the performance of one model compared to another; they must be compared to an observed reference. Furthermore, talking about performance is not very clear here, as it is not a very specific objective.
Abstract : “To our knowledge, this is the first application of the sMEV distribution to time series from a convection-permitting-mode => this is wrong, see these references below, among others:
Dallan, E., Marra, F., Fosser, G., Marani, M., Formetta, G., Schär, C., and Borga, M.: How well does a convection-permitting regional climate model represent the reverse orographic effect of extreme hourly precipitation?, Hydrol. Earth Syst. Sci., 27, 1133–1149, https://doi.org/10.5194/hess-27-1133-2023, 2023.
Dallan, E., Marra, F., Fosser, G., Marani, M., & Borga, M. (2024). Dynamical Factors Heavily Modulate the Future Increase of Sub‐Daily Extreme Precipitation in the Alpine‐Mediterranean Region. Earth’s Future, 12(12). https://doi.org/10.1029/2024ef005185
Vohnicky, P., Dallan, E., Marra, F., Fosser, G., & Borga, M. (2025). Future precipitation extremes: Differential changes from point to catchment scale revealed by a convection-permitting model ensemble. Journal of Hydrology, 662, 133822. https://doi.org/10.1016/j.jhydrol.2025.133822
Lompi, M., Marra, F., Deidda, R., Caporali, E., Borga, M., & Dallan, E. (2025). Non-stationary frequency analysis of long-term convection permitting simulations reveals sub-daily extreme precipitation changes in central-southern Europe. Advances in Water Resources, 205, 105071. https://doi.org/10.1016/j.advwatres.2025.105071
Generally speaking, as soon as I read an abstract "for the first time", I think to myself that the results of an article are potentially overstated.
The following are specific comments mainly related to methodology, as I believe these points need to be clarified before analyzing in detail the results.
Page 4, line 95. It is somewhat surprising to read that the scenario is not suitable because it is too pessimistic. A little more context is needed here to explain why this scenario was selected in this context.
Page 5, line 115. It is very good to cite the sources for the codes used. However, it would be interesting to specify what changes have been made and possibly produce the modified code.
Page 5, line 116 The lmoment method does not focus solely on extreme values. Above all, the method allows for a more robust estimation of the parameters. This needs to be changed here.
Page 5, line 117 There are also regional approaches that provide much more robust estimates for parameters of frequency models compared to pixel-by-pixel estimation. Even though such approaches are not used here, I think we should include this methodological warning.
Page 5, line 120. I think some comments should be added about the difficulty of working with 200-year return periods calculated from 30-year time series, particularly for highly variable precipitation extremes. I think this is a highly questionable methodological choice given the methodology and data available.
Page 5, line 123. This is my main issue with the methodology here. I don't see how repeating by bootstrap observational data over a long series will give more confidence in validating one model over another. I think the methodology here is not suitable for comparing the two distributions. To compare distributions, you need to use observational data and compare the fit to the observed data and calculate, for example, the errors between observed and simulated quantiles for the two distributions. You can also look at the confidence interval produced by the two approaches with the two distributions. In addition, as pointed out by the authors, this bootstrap approach does not take into account temporal dependence or possible trends at all. So it is difficult to see how this approach can be adapted to model comparison.
Page 6, line 147. The GEV parameters are associated with temperature as a covariate. However, there is no justification of the added value of this covariate. For example, a deviance test could be used to verify the added value of this covariate compared to a stationary model. Otherwise, the authors do not provide sufficient guarantees to verify that the principle of parsimony is satisfied.
Furthermore, it is rather difficult to understand why, in the context of trend detection, time is not used as a covariate, which is the most commonly used approach.
Page 8, line 215 I don't understand the consistency here between using a GEV with a linear dependence on temperature, an SMEV model with an exponential dependence, and then explaining in this section that ultimately a linear dependence is used.
Page 11, line 258. It is unclear how the root mean square error is calculated here. This concerns the observed data? or the simulation approach proposed in the methodology?
Page 15, line 296. Could this result be influenced by the covariate temperature? I don't understand why the quantiles are not compared with the stationary models here.
Page 18, line 400 This sentence is incomprehensible. The authors write that they do not make scenarios about extreme precipitation events, yet that is exactly what they are doing. Explaining that this work is on the “challenges and opportunities of stationary and non-stationary distributions” does not mean much.
Page 19, line 431. “, which is why more complex versions are needed to represent strong change” = what does it mean ?