Review for “Enhanced Evaluation of Sub-daily and Daily Extreme Precipitation in Norway from Convection-Permitting Models at Regional and Local Scales “ by Xie et al.

The paper presents an evaluation of extreme precipitation from two regional climate models; one is a regional model at 12km resolution, the other is a convection-permitting model at 3km resolution. The evaluation is based on different observational products: two grid products at 1day and 1hour temporal resolution, and a network of rain gauges (about 190 at daily resolution and 10 at hourly resolution). Seasonality and relation with elevation are also explored, at regional (by contrast with the gridded product) and local scale (by contrast with rain gages). The main findings indicate generally better performance for the HCLIM3 than the HCLIM12 model, more clearly at hourly resolution.

The study is of interest on the general topic of evaluation of extreme precipitation from convection-permitting models, which may help in better understanding how to use them in practical applications. In my opinion, it gives an incremental advancement in this field more than novelty, considering the regional scale (Norway) and the use of metrics commonly used in these kind of studies (Rx1d, Rx1h, return levels). It is well written, but I found difficult to get the main messages on the results because of the total length, number of figures and panels. The topic is of interest, and fitting the journal scopes, but I suggest a few major revisions and some minor before publication in HESS. My comments are listed below.

• Major 1. You used 8 regions. I wonder if less can be used, pooling together smaller ones. I say this based on two considerations: 1) extension is very different across the regions, and regional evaluation of models are then based on quite different number of grid points (for example, how many grid points for the two small regions in the south?) and number of daily rain gages (just 4-11-14 for three regions!); 2) it is difficult to follow the explanations and figures with comparisons on 8 regions, 2 models, 4 seasons, 2 durations … and get a message on the results; maybe having less could help.
• Major 2. Regional scale is here referred to the analysis using the gridded products; local scale is referred to the analysis based on rain gages. They show different results, but I wonder how much this is due to the use of different observation products. How is SeNorge-models comparison sampled on the same rain gage points? Or, how is seNorge compared to rain gages? Differences you highlight in your text (e.g lines 337, 352, 384, etc) could be due to the use of a different observational benchmark. Moreover, when comparing gridded products and climate model, you compare values from a same size grid (12x12km), when comparing model and rain gages, the comparison is made on a 12x12km grid and a point measurement. I suggest to add further analysis comparing seNorge with rain gages, or extracting the comparison between seNorge vs models on the same locations of rain gages, or to add  some considerations in the discussion.
• Major 3. Biases (e.g. figure 2, 6) are shown in mm (absolute differences). Maybe relative differences (modelled-observed)/observed could be more meaningful considering that precipitation has a big range across the regions: a bias of 5 mm is different on a 30 mm or a 80 mm daily precipitation! My suggestion is to update maps and plots of biases with relative bias (%), and to revise description of results and comments on “magnitude” of bias based on this.

Minor comments

• title: I suggest hourly in place of sub-daily, considering that you just evaluate 1h
• Line 120: these results on orographic effect “were based on the annual maxima”… yes. But also yours are based on Rx1d (see line 222-223). So … why do you highlight these about other studies at line 120, if then you do the same? I suggest to remove.
• Line 121-122. “The dependence on seasonality … need the evaluation based on season”. Of course! Maybe you wanted to tell something different here and I didn’t get it. Please calrify/modify.
• Line 195. Not clear: you say here you averaged the indices in the region … then in caption of figure 2 you write the bias is calculated at each grid point (this makes sense to me). So, when do you use the averaged indices?
• Line 206. Specify somewhere in the section that this is done for daily data (both gridded and rain gages), while just on 10 rain gages at 1h duration. More importantly, later in the text (lines 367, 372,..) you mention uncertainty … and it is never explained before in the paper how it is evaluated. Add it in the methodology.
• Figure 2 (same for figures 6 and 7). The caption mentions absolute bias but describes a relative bias calculation. Please correct.
• Figure 2c (same for figures 6 and 7). I suggest to add another row with the regional bias for annual Rx1d, not just seasonal, in order to have a synthesis of what is shown in the maps in panel a.
• Figure 4. Same y-axis limits could help in comparing the bias… and maybe you can revise the description of results better considering the different magnitude of the bias (example at lines 290-291).
• Figure 4. I can’t understand why Northern-Coastal has so big bias for HCLIM3, considering that figure 3 shows a tendency of underestimation of the empirical distribution, similar to Northern-Inland, for which you find big underestimation of return levels. And for the Norther-Inland, why underestimation of return levels is bigger for HCLIM3, while the distribution in figure 3 show more underestimation for HCLIM12? … please check the correctness of the results.
• Line 370. For 50yr return-periods they seem identical, not larger bias for HCLIM3. I would remove it
• Line 439-440. This is based on just 10 points. This can’t be considered a general finding, I suggest to mention the limit of the analysis.
• Line 483, section 4.5. You show the slope of precipitation with elevation as absolute value, mm/km. I strongly suggest to calculate and show it as relative slope, for example with respect to the average value of Rx. Because 1mm/km has a different magnitude for Rx1d and Rx1h. Then I suggest to revise your discussion considering this … (e.g. I see very weak relation of Rx1d with elevation, so I’m sure you can really speak about reverse orographic effect …also at line 629)
• Line 494. “Significant”? Based on a specific test? Maybe “relevant”…
• Line 561-562. Not very informative consideration …. Could you elaborate more on this? Or delete …
• Line 580-582. I see here two contrasting points. 1) You mention underestimation for return levels, but for Rx1d in figure 8 I see bias around zero, while for Rx1h you have evaluation on just 10points. 2) Then you say this is in line with results in Malawi (!!!!) finding overestimation. I can’t really understand your reasoning here.
• Line 587. I can’t understand the meaning of “weakening the superior” …
• Line 635. Dallan et al. 2023 analyzed annual Rx1d: this can’t be related with the seasonal Rx1d. I suggest to rephrase in some way: ”An unclear relation of Rx1d with elevation at regional scale was also seen from the study of Dallan et al. (2023), in which, they analyzed annual Rx1d based on CPRCMs and in-situ observation over Alpine”
• Line 640. I suggest to add a few recent references on orographic enhancement at daily scale observed in different regions (e.g. Formetta et al.  2021 https://doi.org/10.1016/j.advwatres.2021.104085 and Amponsah et al. 2022 https://doi.org/10.1016/j.jhydrol.2022.128090); same at line 651 for the reverse orographic effect, adding also Formetta et al 2021, considering they explored durations from subhourly to daily.
• Please also revise your conclusions accordingly to the modifications you will do in the revised version of the manuscript

The study addresses the added value of convection-permitting modeling in extreme precipitation from regional to local scale, and the ability of convection-permitting model (HCLIM3) in reproducing orographic effects on precipitation in a topographically diverse country like Norway, by comparing it with those where convection is parameterized (HCLIM12). The evaluation considers both gridded datasets and in-situ observation (10 hourly rain-gauges and 192 daily rain-gauges), and provides a robust evaluation of the performance that HCLIM3 offers in the context of extreme precipitation modelling. A key contribution of the paper is its examination of the magnitude, frequency, seasonality and orographic effect of hourly and daily extremes between HCLIM3 and HCLIM12 at both regional and local scales. The results show that HCLIM3 provides added value over the HCLIM12 model in most regions of Norway, particularly at the hourly scale. They highlight the critical role of the convection-permitting regional climate model (HCLIM3) in capturing the characteristics of extreme precipitation compared to HCLIM12.

This work holds great value for the application of regional climate models to simulate and predict the severe meteorological hazards, particularly in the context of localised extreme weather conditions. It provides critical benchmarks the performance of convection-permitting model in local extremes simulation.

Overall, this paper offers significant value and is suitable for publication in HESS. The topic is of interest and fits the journal scope, but I have several suggestions and comments before publication in HESS:

Major comments

1. Ten hourly rain gauges is a bit uncertain, so I suggest that you use it as additional remarks in the daily rain gauges section. You can delete it or remove some results related to hourly rain gauges to the Supplement, and indicate the uncertainty in the discussion.
2. Conclusions should be drawn with caution, especially for hourly scale. Given that the length of the hourly gridded dataset is only nine years and that there are only ten hourly rain-gauges, it is therefore essential to exercise particular caution and awareness when considering the conclusions drawn from hourly in-situ observation.
3. The text uses a lot of acronyms for HCLIMs, but you don't define it. Please define this acronym at the first instance of its use.
4. Although this manuscript is well written, it should be edited further to ensure clarity for the reader. This should include attention to sentence structure, as well as minor spelling and grammatical errors.

Minor comments

Figure 2, 6, 7: What do the dashed lines represent?

Figure 10, 11, 12, 15: Replace the “Station ID” with “Name”. Revise the corresponding text.

Figure 13-15: the unit is missing.

Figure 16, 17, 18: The title of the figure is unclear, please revise it.

Table 1: Unit of the “Elevation” is missing. Check and move to supplement. If it is possible, a corresponding Table for the detail information of daily rain-gauges is necessary.

Line 200 and Figure 5, 9: Why statistic the frequency exceeding 10, 15 and 20 mm, how to define the threshold? Given the focus of your paper on extreme precipitation, it would be advisable to remove these results.

Line 49-51: Please rewrite the sentence.

Line 62: “improve the estimates of short-duration extremes”. Please correct it.

Line 64-65: Replace “atmospheric deep convection” with “deep atmospheric convection”.

Line 84: Replace “coarser-scale model” with “a coarser-scale model”.

Line 87: Delete “were”.

Line 128-135: Please use either CPRCMs or CPRCMs consistently.

Line 132-134: “The main objectives of this study are (1) enhance…; (2) assess…”. Please revise it.

Line 161-165: This contradicts the AR argument as ARs are always associated with extratropical cyclones.

Line 177, 178, 264: Replace “Norway mainland” with “Norwegian mainland”.

Line 193: Please elucidate the rationale behind the decision to resample to HCLIM12 (~12 km). What are the distinguishing factors between resampling to HCLIM3 (~3 km) and the aforementioned approach?

Line 207-209: This is quite confusing. Please write it.

Line 224: Replace “relations” with “relationship”.

Line 276: The sentence is not clear. Please rewrite it.

Line 385-386: The sentence is not clear. Please rewrite it.

Line 386: Replace “in the middle-coastal” with “in the middle-coastal region”.

Line 411-413: The sentence is not clear. Please rewrite it.

Line 431: Replace “that of HCLIM12” with “that from HCLIM12”.

Line 481: “The CPRCM excels…...”. Check and rewrite it.

Line 560-562: The sentence is not clear. Please correct it.

Line 567-569: The sentence is not clear. Please rewrite it.

Line 579: Replace “and found…...” with “finding……”.

Line 591: Replace “may attribute to” with “may be attribute to”.

Line 594: “attributable to ……”. Check and rewrite it.

Line 596: “especially at highly localized local scale”. Check and rewrite it.

Line 598: Replace “recognize” with “acknowledge”.

Line 599: “The performance of RCM ~10 km in representing sub-daily rainfall was limited.…..”. Please correct it.

Line 637: Replace the “shows ” with “show”.

Line 659: Delete “underestimation and”.

Line 692: Replace the “HCLIM3 also have” with “HCLIM3 also has”