Review of the paper “Bias-adjusted projections of snow cover over eastern Canada using an ensemble of regional climate models” by Bresson et al.

In this paper, Bresson et al. present bias-adjusted projections of snow water equivalent (SWE) over Eastern Canada. They applied bias-adjustment methods from the climate community to snow outputs from an ensemble of regional climate models using the ERA-Land SWE as a reference gridded product for debiasing. Several indices were then derived from the bias-adjusted times series of SWE such maximal annual SWE, snow cover duration, date of snow cover onset, … The authors finally presented how these indices are projected to change in different subregions of Quebec during the 21^st century.  

The topic of this paper is relevant for stakeholders and decision makers interested in the future of the snow cover in Eastern Canada. However, this paper could have reached a larger audience among the snow community if the authors had better explained their debiasing methodology and quantify its impact on the projections of the different SWE-related variables as detailed in my general comments below. This paper would have also benefited from an evaluation of the ERA5-Land SWE over Quebec using in-situ snow observations to better justify the choice of ERA-Land as the reference gridded snow product for this study. Therefore, at this stage, major revisions are required before this paper can be considered for publication in The Cryosphere. My main comments are listed below as general comments and are followed by specific and technical comments.

General comments

1. The introduction of the paper is rather short and does not present well the scientific context of the study. It should give more details about the bias-adjusting methodologies that are widely used in climate services. These methods are not necessarily well known by non-experts in climate projections. In addition, the introduction could give more detail about the challenges associated with the debiasing of snow variables that present a strong seasonality. The authors mention L 44 that the only debiasing method for SWE has been proposed by Michel et al. (2023). They could explain briefly what the characteristics of this method are and why does it work for snow variables. At the end of the introduction, the readers need to understand why bias-adjusted variables are crucial for climate services and the associated challenges for snow variables.
2. The authors use the SWE from ERA5-Land as the reference gridded product for debiasing. They only refer to performances reported in Kenda and Fletcher (2025) and Mudryk et al. (2024) to justify their choices. However, this justification remains vague. For example, they refer to the evaluation of ERA5-Land for different ranges of elevation detailed in Kenda and Fletcher (2025). However, this evaluation concerns Canada as a whole, including regions such as the Prairies or the Arctic where the snow conditions differ significantly from those found in Quebec. For this reason, I recommend the authors add an evaluation of the performances of ERA5-Land across Quebec using reference SWE measurements from the CanSWE dataset complemented by SWE data collected by the province of Quebec. Such rich dataset would allow a comprehensive evaluation of ERA5-Land in Quebec that would (i) strongly justify the choice of ERA5-Land as the reference product for debiasing and (ii) help identifying regions of lower performances for ERA5-Land where the result of the debiasing should be considered more carefully. In itself, a solid evaluation of ERA5-Land would be a very interesting outcome of this paper.
3. A key component of this paper is the method used for the bias adjustment of SWE from regional climate models. In its present form, the paper does not explain well and illustrate well why applying a bias adjustment is crucial before deriving relevant projections for different snow indices. Section 2.4 should be heavily revised to better explain the bias-adjustment method. The following points should be considered:

• It is not clear how the method proposed by Michel et al (2023) addressed the issue associated with the seasonality of snow mentioned in the introduction as one of the main challenges for the bias adjustment of snow variables.
• It seems that the bias adjustment method allows for a downscaling from the native resolutions of the RCM (which are not mentioned in the paper) to the resolution of ERA5-Land. Can the author explain how such downscaling is possible?
• A pre-processing step is applied to the SWE time series during the melting season (P 6 L 135-142). It would be very valuable for the reader to have a figure that illustrates the impact of this pre-processing step. In addition, is this pre-processing applied to each of the grid cells?

1. I understand that the authors want to focus their analysis on the bias-adjusted projections as part of group that provides climate services to stakeholders. However, from a scientific point of view, I recommend the authors to quantify if the projections of the different snow indices would have been different without the bias adjustment. Such analysis would highlight the importances of carrying out such bias adjustment when considering snow variables. For climate service, it may be obvious for certain variables such as near surface temperature, but it seems that bias adjustment is less usual for snow variables due to methodological challenges. This analysis would be a solid contribution from this paper that would extend beyond stakeholders interested in the future of the snow cover in Quebec.

Specific Comments

P1 L 14: the terminology “the northern part of the northern hemisphere” is rather vague. Can the authors clarify? Maybe give a range of latitude.

P 1 L21: what do they authors mean by “best understood”?

P1 L22: “surface temperature”: is it the actual surface temperature (“skin temperature”) or the screen-level temperature (taken for example at 2 m above the ground)?

P1 L24: it would be highly relevant for the readers to add here references that illustrate how challenging it is to adjust the bias of variables with rare occurrences or strong seasonality.

P2 L 30-35: these sentences contain several statements that should be supported by appropriate references. For example, the statement “to better reproduce … the processes such as sublimation or ablation” is really vague and must be supported by references.

P 2 L35: Offline simulations with snowpack schemes are often carried out at continental or global scales (such as the ERA5-Land product used in this study or the Crocus-ERA5 dataset (Ramos Buarque et al., 2025).In this context, I recommend the authors to rephrase the sentence “Consequently, this method could be better adapted for specific purposes at a local scale”

P 2 L 43: the term “snow cover” used here is confusing since it is already widely used in the paper to refer to snow in general. Maybe use “snow cover fraction” since it is the variable of interest in the paper of Matiu and Hanzer (2022).

P 2L 48-49: can the authors explain briefly what are the problems that arise with snow simulations at high elevation?

P 2 L59: It would be interesting to know the mean elevation of the different subregions considered for the analysis. In particular, it would illustrate well the contrast between Southern Quebec and SLRV.

P3 L 71: what do the authors mean by “flexibility”? Would it be possible to reformulate to be clearer?

P 4 L 89: what do the authors mean by “mismatch”? Between which datasets? What was the nature of this “mismatch”?

P4 L 91-92: this sentence should be reformulated since Figure 3 in Kenda and Fletcher (2025) presents an evaluation of the SWE from ERA5-Land across Canada (including region below 50N).  Kenda and Fletcher (2025) did not only evaluate ERA5-Land in northern Canada above 50N.

P 5 L 103: how many simulations were considered in this first ensemble?  

P 5 L 117: Why are the authors using the argument about the availability of SWE observations to justify focusing on the region below 50 N in their selection criteria? Indeed, SWE observations are not used in this study to evaluate ERA5-Land (see my second general comment) and are not assimilated in ERA5-Land.

P 5 L 119: how many candidates were present in the initial ensemble? Such information is interesting to better understand how strict the selection criteria were.

P 5 L 119-120: it would be good to know what the selection criteria were in McCrary et al. (2022) to justify why it makes sense to compare the two ensembles.

P 6 L 147: does the value of 375 mm refer to ERA5-Land?

P 7 L 157-159: Has the bias correction already been applied when presenting the results for the two ensembles?

P 12 Figure 9: The dots on the different subplots seem to be rather noisy, especially for the northern and central domain. Is it because of very few events (even only one) over the 30-yr period are considered when computing the mean duration of noSCseq? Showing results aggregated longer time periods can potentially reduce the noise and make the figure easier to read.  

Technical Comments

Text

P2 L 32: “CROCUS” is not acronym and can be written “Crocus”.

P 2 L 46: “water content in the snowpack” could be confusing. Maybe use “total water content of the snowpack” to make sure that it does not only refer to liquid water content in the snowpack.

P2 L 55: It could be worth mentioning the other Canadian provinces that are included in the simulation domain.

P 5 L 104: the year is missing for the reference to Mearns et al.

P 5 L105: Explain the meaning of the acronym “SM”. It should be changed throughout the document. I also recommend the authors to mention to which specific table or figure they are referring to in the Supplementary Material.

P 5 L 108: the term “melt” or “melting” is often preferred to “thaw” when referring to snow.

P 5 L 131: please double check to reference to (Themeßl et al., 2012). Is the family name written correctly?

P 6 L 144: this sentence can be included in the previous paragraph to avoid having a paragraph made of a single sentence.  

P 6 L 148: explain that the names of the regions such as Charlevoix, … are shown on Fig. 2.

P 16 L 264: add the corresponding DOI.

Figures

Figure 10: the different levels of transparency are not visible in this figure.