Review of Wouters et al. 2025

Overall, the paper is well written, and the statistical technique developed appears robust. However, there are several issues that need to be addressed before I can recommend this manuscript for publication.

Major Comments

The authors have chosen 5 sites to evaluate EXSoDOS and the manuscript clearly mentions why these cases were selected. However, in my opinion only five cases globally may be insufficient to fully assess the robustness of the approach. I recommend expanding the analysis to include 20–30 stations, with representation from additional regions. Specifically, it would be valuable to incorporate sites from North and South America, as well as Australia. Including a few stations located on islands and near the Southern Ocean would further strengthen the geographic diversity of the dataset. Within India, the current focus on Puri (a coastal city) could be complemented by selecting sites from different geographical settings, such as an inland city like Delhi or a location near the Himalayas.

Section 3.1: The current description lacks sufficient detail. While the authors state that there are differences between the four columns in Figure 3, these distinctions are difficult to visualize due to the way the figure is presented. I recommend enhancing the analysis by including one or two additional metrics and discussing the potential limitations of the Perkins distribution overlap score. Similarly, in Section 3.2, I suggest incorporating relevant statistical metrics (perhaps in the form of a table) to quantitatively assess aspects such as ‘increase’, ‘decrease’, ‘less extreme’, and ‘shift of distribution’.

The statistical framework presented in this manuscript is built upon several underlying assumptions. I recommend that the authors include a table listing all these assumptions, accompanied by brief descriptions for each.

Minor Comments

L22: “…generally underrepresented in climate projections.” Add 2-3 references.

L38: “finer resolution grids”. It might be good to mention the resolution.

L53: “…normalized and correlated with each other.” Kindly add one sentence with a short explanation.

L66: “EXSoDOS runs quickly…”  Please add a metric here. Or maybe add “quickly (see later)…”

L87: Is there any reason for the 50:50 split? Citation to a previous literature might be helpful.

L140: How sensitive are the results to the choice for 2 additional months.

L149: Are there existing literatures which support the choice of an exponential profile?

L192: How sensitive are the results to the choice of ‘c’? Can the authors include a figure?

L213: I would like to see a figure/chart before and after detrending.

L305-320, 325-330: Kindly add a few more citations for each case.

Figure 3: Needs a legend.

Figures 3, 5, 6: I recommend using Kelvin as the unit of temperature.

Figure 5: Check units of ‘wind speed’ and ‘precipitation’

Figure 6, 7: Kindly consider using one common legend for the entire figure.

This manuscript proposes a new stochastic model to improve the prediction accuracy of climate extremes. The methodology is innovative, and the manuscript is well-written and easy to follow. However, I believe the following issues should be addressed before publication on Geoscientific Model Development.

As the authors mentioned in Lines 213-215, the statistical relationship was assumed to remain unchanged under climate change. To test the robustness of the relationship, could the authors conduct cross-validation (e.g. 5-fold or leave-one-out) to evaluate the stability and generalizability of the statistical relationship?

In addition, I am concerned about the representativeness of the five stations selected (Section 2.6.1). Why the five cases were selected to show the general utility of the model? What are the Plant Functional Types of these stations? Understanding the PFTs could help assess whether the model performance is influenced by land-surface characteristics.

This manuscript introduces EXSoDOS, a stochastic point-scale downscaling framework for representing changes in weather extremes however, several methodological assumptions, validation choices, and claims of novelty need strengthening before publication.