Reyniers et al. 2024 evaluated and bias-corrected the UKCP18-RCM-PPE dataset and discussed the performance of both the original and bias-corrected data. I acknowledge that bias correction (BC) is important for impact studies. Effective BC should ideally reduce model biases while preserving key model benefits, such as climate change trend projections and spatial details that might not be captured by observations. However, this study does not appear to introduce a new BC method or provide in-depth insights into the limitations of exiting BC approaches. Similar BC techniques have already been widely applied in many impact studies, which somewhat limits the novelty of this study and the dataset produced.

I have several suggestions for improvement:

• Line 35-40: This is a good and important discussion on key limitations of bias correction. However, the methodology used in this study did not addresses these challengesyou’re your BC approach did help to “address the origin of model errors”, that would be a significant contribution to the community. As it stands, the manuscript does not seem to offer solutions for tackling the root causes of model errors.
• Line 66: “simple BC methods used in CHESS-SCAPE”: what methods they use?
• The data you corrected is from a perturbed physics ensemble, which is designed to both explore the influence of parameter variations on simulations and to reduce uncertainty. It would be highly valuable to examine how bias correction affects the ensemble results. For example, does using a single observation dataset to correct multiple ensemble runs impact the ensemble’s representation of uncertainty? Additionally, it would be useful to see a discussion on the performance of individual ensemble members before and after bias correction. Such an analysis could provide insights into how bias correction interacts with model parameterizations and the ensemble spread, and provide physical interpretation of your results.
• The overestimation of dry-day frequency is a common issue in climate models, often linked to the drizzle effect. I recommend addressing the drizzle effect first, as this might improve the accuracy of subsequent bias corrections and separate this systematic bias from others.

Two simple bias correction methods have been addressed to investigate their impact on the climate model projections. Overall, these are interesting results. However, I think some major concerns and questions need to be addressed.  Here, I list some of the comments/questions that were unclear.

Main comments

1. This paper evaluates two relatively simple statistical methods for correcting climate model output directly. The results indicate that bias correction techniques can mitigate biases related to the indices utilized in this study. Nonetheless, my principal concern is the paper's originality. Numerous studies have already been published that compare various statistical and machine learning methods across different domains and temporal contexts, ranging from historical data to future projections. Consequently, I do not believe this paper contributes significantly new information relative to existing literature, particularly given that available published data could offer additional insights. While the journal is dedicated to data publication, it is essential for this paper to either introduce a novel methodology or provide a comprehensive analysis of each method, highlighting the advantages and disadvantages of their application in specific circumstances. Merely comparing simple methods that produce comparable results for future projections may not provide substantial value.

2. The manuscript lacks a comprehensive explanation for the preference of two simpler methods over more sophisticated approaches. The selection of the degree of bias correction should depend on the specific application, as advanced techniques have been shown to produce greater improvements, particularly in the context of extreme events. While quantile mapping and simple climatological mean correction have demonstrated their advantages in preventing excessive correction based on observational data, they may still permit the persistence of biases related to low-frequency variability, which can complicate the direct correction of surface variables in climate model outputs.

Numerous studies have employed sophisticated bias correction across various time scales to adjust the outputs of GCM and RCM or the boundary conditions for RCM inputs, with the objective of enhancing the accuracy of simulations for extreme and compound events. It would be advantageous to incorporate additional references that pertain to bias correction prior to addressing the limitations of existing studies. Furthermore, it is essential to provide detailed information and explanations regarding using simpler methods in the introduction or conclusion to ensure a thorough understanding of the techniques ranging from simple to sophisticated methods.

- Correcting outputs

Wood AW, Leung LR, Sridhar V, Lettenmaier D (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Change 62:189–216

Cannon, A. J. (2018). Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables. Climate dynamics, 50(1), 31-49.

- Correcting RCM input full variable fields

Bruyere CL, Done JM, Holland GJ, Fredrick S (2014) Bias corrections of global models for regional climate simulations of high-impact weather. Clim Dyn 43:1847–1856

Kim, Y, Evans, JP, Sharma, A (2023). Can Sub‐Daily Multivariate Bias Correction of Regional Climate Model Boundary Conditions Improve Simulation of the Diurnal Precipitation Cycle?. Geophysical Research Letters, 50(22), p.e2023GL104442.

- Software for correcting climate model variables

Cannon, A.J. (2016). Multivariate bias correction of climate model output: Matching marginal distributions and intervariable dependence structure. Journal of Climate, 29(19), pp.7045-7064.

Kim, Y., Evans, J. P., & Sharma, A. (2023). A software for correcting systematic biases in RCM input boundary conditions. Environmental Modelling & Software, 168, 105799.

3. The authors have undertaken corrections for three variables: precipitation, temperature, and potential evapotranspiration (PET). It would be beneficial to clarify the methodology employed for correcting PET. Did the authors correct PET derived from the raw variables directly, or did they utilize the adjusted variables in the PET calculations? As indicated by the authors, PET is influenced by several variables generated from the climate model, including specific humidity, pressure, and temperature, which can be modified by adjusting surface variables interconnected with these factors. Correcting these variables statistically, without accounting for the physical relationships among them, may lead to inconsistencies and produce unrealistic results.   

 

Specific comments

L66. “the simple methods …” It would be beneficial to provide more details about what these methods entail.

L68. “the quantile mapping (QM) method outperforms … the standards deviation and percentiles.” Quantile mapping (QM) can outperform simple mean correction for variance since the latter does not address the standard deviation or percentiles. I recommend incorporating more details about bias correction techniques, including methods like simple mean and standard deviation correction. This will help justify the use of empirical QM when publishing datasets for broader applications.

L82. “the trend-preserving BC method.” It is essential to justify the trend-preserving method for future projections, as climate models also contain biases in trends.

Figures 9 and 10. I recommend modifying the figures to use a bias map instead of presenting each one individually, as the differences are minimal.