This manuscript develops and evaluates a ML-based surrogate for the MSKF convection scheme in WRF. The text is well written and the approach seems novel. I am not an expert on ML, so my comments are all on the atmospheric modeling/parameterization side of the work. My major comments are as follows,

1. Why is the goal to emulate what a conventional convection parameterization does in the first place? I ask because the ML scheme presented in the paper seems designed and trained to emulate MSKF performance at 5-km resolution. Why not just run MSKF directly? What's the need to run this ML-based emulator? Is it cheaper? Is it better? Please elaborate a little more on that.

2. Is the performance of MSKF good for the area and cases the authors are interested in? If so the authors should provide good evidence for it.

Minor comments (mainly on the introduciton part, which is otherwise quite well-written) follow,

1. Line 37: "Nevertheless ... These conflicting findings typically...", Did the Schwartz paper also use CP in their simulations or not? Either way I don't quite see the conflicting part here ...

2. Line 102: "Furthermore, all previous studies have predominantly focused on using CP schemes in GCM models for climate forecasting. Moreover, the choice of CP schemes significantly influences the uncertainty in precipitation forecasts within weather forecasting models. The complexity of the CP schemes also surpasses those applied in climate models (Arakawa, 2004)." I don't think the last statement is generally true. Also the logic doesn't seem to flow among these few sentences.

This study provides an ML multiscale Kain-Fritsch convection parameterization and applies it into WRF. The results indicate that the ML parameterization yields comparable outcomes to the original WRF simulations. However, the motivation behind this study is unclear, and the evaluation of the ML parameterization lacks thoroughness. As a result, I recommend a major revision to address these issues.

Major comments:

1. This study focuses on the development of a new machine learning (ML) convection parameterization. However, the training dataset used in this study is derived from the WRF simulation, which may not provide significant benefits for the ML parameterization. The dataset essentially serves as a surrogate for the old parameterization. Instead, a more suitable choice for the dataset could be from a super-parameterization or cloud resolve model, as it could help reduce uncertainties and enhance the ML parameterization's performance.
2. It would be beneficial to conduct a comprehensive evaluation of the ML convection parameterization, considering factors beyond just the mean state. In addition to assessing the accuracy of tendencies, it is important to evaluate the parameterization's ability to accurately represent triggered convection. Furthermore, examining the diurnal cycle of precipitation and interpretability of the ML parameterization can provide valuable insights. Additionally, when coupling machine learning parameterization with dynamics, it is crucial to investigate potential issues with simulation stability. Have you tested the parameterization for longer simulations to assess its stability over extended time periods?
3. In this study, the machine learning approach performs multi-task learning, simultaneously handling trigger function classification and tendencies regression. It would be valuable to conduct an ablation study, where the classification and regression tasks are separately evaluated and compared with the multi-task learning approach. This analysis can provide insights into the individual contributions and effectiveness of each task, helping to further understand the benefits and limitations of the multi-task learning approach.
4. In Figure 4, is it correct that r^2 is the coefficient of determination? It seems unusual that although the points in (a) are widely scattered, the r^2 value is very high. Also, while the RMSE difference between (a) and (b) is large, their r^2 values are quite close.

Minor comments:

1. Line 145:  Could you please explain the rationale for selecting these particular variables?
2. Line 170: could you explain the trigger condition base on lifting condensation level (LCL), convective available potential energy (CAPE), cloud top and base heights, and entrainment rates?
3. Line 45: ‘hsave’ to ‘have’
4. Caption in Figure 1: ‘5’ to ‘5°’
5. Line 149: it could be better to add these 4 variables into Table 1.
6. Line 182: remove ‘data’