This study proposes a Comprehensive Index that considers both performance and uncertainty, using 11 CMIP6 GCMs and compares bias correction methods across six continents. Such an approach can be evaluated as a highly significant method for GCMs and bias correction, which are essential for future predictions. The reviewer, therefore, would like to recommend that this manuscript be returned to the authors for major revisions. My comments are given below;

The authors need to address several key questions:

1. Why was only the historical period used? If there is a specific reason, it must be clearly stated.
2. Developing a comprehensive index is a significant strength of this study. However, there is insufficient explanation of the differences from previous methods to highlight its originality. Please address this. Additionally, the comprehensive index appears to be overly limited. Such a restrictive methodology may reduce the efficiency of the approach, which warrants further discussion.
3. DQM performs worse overall compared to other methods. The reasons for this must be explained. However, as QM methods are not newly proposed in this study but adopted from existing ones, it is unnecessary to deeply analyze the cause (this could be a topic for a future study). A brief explanation in the context of specific climate phenomena should suffice.
4. The authors used entropy as a criterion weight for TOPSIS. Is there a reason for this choice? Since MCDA is widely used in other fields, incorporating more complex weights might be more appropriate.
5. Model performance can vary significantly depending on the climate zone, but in most cases, the performance is reported to be very high (e.g., EVS: 0.98 (DQM)). Such results indicate exceptional performance. This should be mentioned in the Discussion section.
6. Why was only ERA5 selected for comparison? It may not pose a significant issue without a valid reason, but considering multiple reference datasets is essential for evaluating performance.
7. Why does DQM yield higher estimates for extreme precipitation? Could this be because DQM emphasizes variability in the data due to detrending?

This study provides valuable insights into climate change research by comparing bias correction methods and incorporating an integrated uncertainty evaluation. Therefore, sincerely addressing the above comments will significantly enhance the scientific value of this study. Additionally, the findings are anticipated to be actively applicable to new stages of GCMs, or models developed after CMIP7.

https://onlinelibrary.wiley.com/doi/10.1029/2021EF002240

https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022EF002830

This paper uses historical CMIP6 simulations from 11 climate models to intercompare different forms of quantile mapping bias correction. Mean statistics of daily precipitation values are the focus of the effort, though there is a short discussion of extreme daily precipitation. The novel part of the paper appears to be the joint assessment of bias correction using uncertainty (via BMA-derived weights among GCMs) and performance (quantified using a closeness index), combining them into a single comprehensive index. The paper could be much shorter to make its point. Far too much time is spent on individual results with mostly qualitative interpretations that do not adequately support the conclusions. A more streamlined paper focused on demonstrating the methods and showing how they can inform decision-making would be a valuable contribution.

Specific comments:

1. Abstract, lines 19-26, the results should focus not on how these three methods ranked based on daily precipitation but on what this application revealed about the method. How sensitive is the ranking to the selection of evaluation metrics? To GCM selection? To weighting of uncertainty vs. performance? To different climatological regions (as mentioned briefly at Lines 666 and 722)? Three QM methods is too small a pool to draw useful conclusions, and the vague language leaves too much unanswered (“QDM outperformed other methods in specific regions…” and “DQM…shows the highest uncertainty in certain regions”).

2. Lines 142-144, the three QM methods are outlined. Were these selected because they represent very different approaches to bias correction? Many other bias correction methods exist, from simple delta change to multivariate. Would a broader selection of methods test your ranking methods more robustly?

3. Line 148-149, the “frequency-adaptation technique” is applied to address potential biases. I did not see this explained further in the methods section. Did this add something beyond what is described by the definitions of the QM methods?

4. Table 2 lists 10 evaluation metrics (though the table caption and line 193 state “seven”). It would help to add columns for the range of values each can take, and what value would indicate a ‘perfect’ fit. An obvious question is why so many somewhat redundant metrics are used (for example RMSE, MAE, MdAE). Could the method be employed with 2 or 3 metrics and perform as well? That would be a useful detail to explore.

5. For clarity, variables should not be used for different quantities. For example, α is used in equation 7 as the scale factor in the GEV distribution, αw is in equation 10 (and doesn’t appear to be defined), and α is used in equation 16 as a performance weight.

6. Figure 1, Since all points in all panels are in the first quadrant, just include that in the figure. That will help readers see the individual points better. That is a pretty standard way to present Taylor diagrams. Also, I would assume this is for the validation period (1997-2014) – it should be noted in the caption.

7. Line 319 (the beginning of section 3.1.2). From here to line 421 offer very little to the aim of the paper. The interpretation of the figures is all qualitative (two examples: for South America, Fig 2 “EQM demonstrated lower JSD values, as well as higher EVS and KGE values, compared to other methods.” and “QDM and DQM also performed well but exhibited slightly larger errors in some regions than EQM.”). First, statistical tests are needed to determine if any differences are statistically significant. Second, a discussion of whether the differences are physically meaningful is needed, such as EVS varying from 0.95 to 0.98 across the region and across methods. Again, if the effort is to rank QM methods, this is far from adequate. If the aim of the study is to demonstrate the application of the method and its sensitivity to methodological choices, then most of this section is not needed.

8. Following on comment 7, Figures 2-7 show the results for different metrics for different continents (again, presumably for the validation period, but that should be clearly stated in figure captions). Figures 2-7 all suffer from the same issues, but I will mostly focus on Figure 2. The color bars are non-linear – while each color segment is the same length, the interval they represent varies widely. For example, the row for Pbias the yellow represents a range of less than 2% while the purple represents over 20%, so a 3% negative bias is indistinguishable from a 20% negative bias. The scales themselves are confusing: for JSD the red colors are the worst skill, while for EVS red is the best skill. Every row is different in this regard. Some indices are unitless and others have units, and that should be represented. Some rows show a wide range of values (Pbias in Fig 2) while others show virtually identical values (NSE in Fig 3, where the colors vary only from 0.98 to 1.0), so where some differences are shown they may essentially be all the same value.

9. The subset of indices shown in Figures 2-7 varies. While the supplemental material may complete the set, it should be consistent.

10. Figure 8 summarizes on a continental scale the performance using each metric. Without any statistical test, the apparent differences cannot be claimed to represent anything. Also, with wide variability (in some metrics) across each continent, a single continent-wide average may not be very meaningful.

11. Section 3.1.3 looks at extreme precipitation. This is not well integrated into the rest of the paper, and only includes a cursory look at continent-aggregated values. It does not fit into any of the rest of Section 3. Line 445 claims differences are “relatively significant” and that distributions “vary significantly”. Since there is no mention of statistical tests, these terms are inappropriate.

12. Figure 9, Why do the scales between the top and bottom rows vary so widely (max of 0.15 vs 0.00024)? Does this figure only represent the 95th percentile like Fig 10?

13. Figure 10, what are the values and units for the x-axis? Again, while these lines appear to be different the densities are extremely close (the y-axis scales cover a very small range) and no statistical test results are presented, so drawing conclusions is limited.

14. Line 464, the weights are “calculated by applying entropy theory”, but that does not appear to have been discussed in the methods section of the paper. This should be mentioned there with appropriate citations.

15. Line 464, While this seems like the more important part of the effort, higher and lower weights are only discussed qualitatively, and while “significant importance” is mentioned (Line 471) and differences are claimed to be “significant” (Line 499) no assessment of significance is shown.

16. Figs. 12,  14, and 16 have many of the same issues as Figure 2-7, noted in comment 8 above.

17. Lines 557-563 discuss Fig. 15. It is claimed that “the EQM ensemble showed the lowest standard deviation across all continents.” The box and whisker plots clearly shows nearly identical results for all methods, and I would be shocked if any of the differences showed any statistical significance. The conclusions here are just not supported.

18. Fig 17 shows the final comprehensive index across all continents. That some apparent differences emerge is due to weighting the uncertainty (as in Fig 15) less. Statistical tests are needed here too, and then it could be explored why the closeness index changes the results so strongly.

Minor comments and typos:

1. Line 42, rather than saying the bias corrections differ in “physical approaches”, “statistical approaches” would be more accurate.

2. Line 47, Correct spelling of Maraun.

3. Line 61, Elaborate on “higher performance.” Is this the same as “better skill”?

4. Line 136, the model resolution “was provided by the institution for research availability.” This is confusing. Is there a citation to add?

5. Line 212, “JSD” is used, where prior to this JS-D is used. The term should be consistent throughout.

6. Line 361, The sentence begins with “In this study, the daily precipitation in Africa was corrected using three QM methods.” This sort of recap appears at the beginning of many sub-sections, and is not needed. Search for “This study,” and rephrase.

7. Line 438-439, ”adjusted by the biased bias correction methods” must be an error