Future Changes in Precipitation Extremes over South Korea Based on Observations and CMIP6 SSP Scenarios

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Assessment of the paper entitled “Future Changes in Precipitation Extremes over South Korea based on Observations and CMIP6 SSP Scenarios” submitted to Water. The submitted version of the file contains several comments from the authors, which makes it impossible to view it in a better way. The paper is interesting and presents an important and current topic, but some limitations must be corrected.

Overall analysis: The paper evaluates four SSP scenarios and a multimodel ensemble of 23 General Circulation Models. It projects significant increases in extreme rainfall events, particularly under high-emission scenarios, with increases of more than 20% expected by the end of the century. Given the regional variability in precipitation changes, the study highlights the need for improved flood mitigation, water resource management, and climate adaptation strategies in response to these projected changes.

Main suggestions:
- Using the average of 23 GCMs as a multimodel may include errors caused by models that do not present good correlations with local/regional measurements, even with BIAS corrections. It is important to highlight this paper's limitations in the Discussion or Introduction.

- What is the article's objective and scientific hypothesis? The authors present the last paragraph of the Introduction as methodological steps (the article's objective is something else); it is important to highlight this because the Conclusions also seem like Discussions and need to be rewritten to respond to the article's objectives.

- Figure 1 lacks scale, North and legend identifying what the regionalized numbers mean.

- In section 2.2.1, is the data measured by national monitoring networks addressed? How many stations were used, and what were the data periods (supplementary documents or appendices)?

- Use more robust statistical indicators for evaluation: RMSE (root mean square error), d Wilmott and NSE (Nash Sutcliffe); Correlation and determination coefficients tend to show the same relationships; Bias and MAE discuss only relative and absolute errors; other indices for statistical efficiency analysis will provide robustness to the results. This is important because low results from chi-square tests in several scenarios indicate even more inadequacies in these models to represent the spatial distribution of extreme precipitation occurrences accurately. This also needs to be incorporated into the Discussion of the article.

- How were the maps generated (spatial analysis)? Interpolators, software, procedures? Detail

- Present the spatial distribution of precipitation from 1985-2014 and 2015-2024 (current), with measured data, and verify if there have been differences between these two periods. The article leaves the reader confused, as it does not clarify the current precipitation behavior (including whether there is annual seasonality – dry/rainy season – or whether the 1000 to 1800 mm rainfall is well distributed throughout the year throughout the country).

- In Figure 3, MAE and BIAS have measurement units (mm/day, mm/month or mm/year, which one would it be?) – it is important to understand the dimension of the error. This is true for the results, as they are presented in section 3.2

- The histograms in Figure 4 should be smoothed by lines for better understanding.

- The Discussion needs to be more robust, with current references/citations, as the article highlights some important evidence: i) observations indicate that the actual precipitation quantiles have remained relatively stable or decreased slightly in recent years (2015-2024), contrasting with the consistent upward trends predicted by climate models. This divergence suggests a fundamental gap in modeling climate change scenarios for extreme rainfall events.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Overall, it is basically a display of the MME forecasts under the four emission scenarios: temporal projection in Fig. 5 and spatial distribution in Fig. 6. Although it is not a real in-depth research, the information it conveys is still useful for policy makers. It may be accepted for publication after a major revision. See the detailed suggestions below.   

1. About the Data Processing, it needs to explain the inverse distance weighting (IDW) interpolation method in more technical details (so readers know it is actually calculated) and list references.
2. Same for the non-parametric Quantile Mapping bias correction method: it needs more detail technical descriptions and list references.
3. For the bias correction, it is suggested to add a figure showing how the bias correction method worked by showing the impact of bias correction (compared the results before and after).
4. Fig. 2: what do those open circles represent? It should be explained in the caption.
5. Fig. 3a: Bias increased, which is not good and should contaminate the forecast signals. It needs to have to mention this for readers to pay attention to. How the bias correction step impact this? The bias correction method didn’t work well?
6. About the “Future precipitation quantiles” section: Are these all model’s projection (all the way to 2100)?
7. Fig. 5: at the beginning it is obvious that the observation remain similarly over years (no upward trend), while model predictions have increasing trend with time. So, how can you believe the model’s upward trend is believable? I know you have said that at the end. Maybe can be emphasized more to alarm readers?
8. Figures and tables’ position is arranged too far away from the related text, it’s hard to reference to each other during reading.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors provided major corrections to the article and strongly justified all comments. I fully agree with the issue of statistical indicators associated with time series; however, the inclusion of RMSE was important. I am in favor of accepting the article in its current form.

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have satisfactorily modified the manuscript based on my suggestions, so I suggest accepting the manuscript for publication at this time.