This article employs the XGBoost model to temporally downscale daily climate data, generating THI data that quantifies the impact of temperature and humidity on cattle. The model training requires 24-48 days, but generating a year's data only takes 3-8 hours. Overall, the topic is interesting. However, I have some concerns as follows:

1. " We opted for the XGBoost model for its computational efficiency compared to Random Forest and other analogous algorithms, specifically for our use case." The authors claim that XGBoost offers higher computational efficiency than Random Forest and similar algorithms in their specific case. Please provide specific comparative values.
2. The spatial resolution of the THI is 0.25°, which seems to be a very coarse pixel size. How can you ensure that the values accurately represent such a large spatial area? Additionally, the input data for the method includes ERA5 and NEX-GDDP-CMIP6 with a spatial resolution of 0.25° as well. Would this method still be applicable if high-resolution data were available?
3. The ERA5 reanalysis dataset provides historical hourly data. Knowing its quality would be helpful. Can you utilize ground-truth data to validate the used variables in the ERA5 dataset? Without assurance of the input data quality, the quality of the trained model cannot be guaranteed.
4. More details on the structure of the XGBoost regressor model are needed.
5. The formula for THI indicates that its value depends on temperature and relative humidity, with the latter being calculable from ambient and dew point temperatures using equations 1-3. This suggests a strong dependency of THI on these two temperature data. An error analysis will be helpful. The author could do an experimental study to explore how errors in these temperatures affect THI accuracy.
6. "The performance of the trained models was assessed using ground truth data derived from the ERA5 dataset." Please provide more details about the ground truth data, including its spatial representativeness and temporal resolution.