This study investigates how environmental variables influence the seasonal variability of methane (CH₄) at high latitudes in the Northern Hemisphere, utilizing satellite observations from TROPOMI along with meteorological datasets. The research provides a valuable contribution to understanding CH₄ variability, identifying key drivers such as snow cover, soil moisture, and soil temperature. Only minor revisions are recommended to enhance the clarity, interpretability, and completeness of the manuscript.

1. Lines 190-195：How to determine the study area by testing? Can you explain it in detail?
2. Lines 325-330: Did you validate the Random Forest model using cross-validation techniques (e.g., k-fold validation)? If so, how consistent were the importance rankings across different training-test splits?
3. Lines 345-350: Since multiple environmental variables (e.g., snow cover and soil freeze-thaw state) exhibit high correlations (r > 0.85), how do you ensure that feature importance rankings are not biased by collinearity in the Random Forest model?
4. Lines 415-420: Have you considered the contribution of atmospheric transport processes to the observed seasonal variability of methane concentrations, particularly the potential influence from emission sources located outside your study regions?
5. Lines 450-455: Soil moisture is identified as a key driver of seasonal CH₄ However, why does it not have the same level of importance in daily variations? Could transient factors like precipitation events or drainage explain this discrepancy?
6. Lines 490-495: The ranking of OH as a CH₄ sink varies between the two feature importance methods (Permutation Importance and RFFI). Why do these differences arise?
7. Lines 580-585: Does the observation that seasonal CH₄ maxima and minima closely align with the timing of complete snowmelt imply that snow cover primarily controls the seasonal variability through its effects on soil temperature and moisture? Could you further discuss this controlling mechanism?

The present study focuses on environmental drives affecting observations of methane total columns in Northern boreal regions. The manuscript is very well written, well detailed and balanced. Direct analysis of satellite observations are valuable, even before inversion or modelling study, as they allow to get coarse insights on how regional environments react to external drivers, especially in Northern Boreal regions where observational data are very scarse. The present study can be considered for publication but needs a redefinition of its scope as it is too narrow at the moment.

I recommend exploring the following axes to justify publication:

1. Impact of TROPOMI product on conclusions
   
   The authors chose the WFMD product to conduct their study. This seems reasonable with latest versions of WFMD. Still, other products (operational and SRON research product, as well as Balasus BLENDED product) can show different patterns and may lead to different conclusions
2. Added value of satellite products
   
   TROPOMI offers a valuable data sets in Northern latitudes, independent from local countries that can limit data access. Still, there exists long-term time series, both atmospheric and flux data in the Arctic, that could offer similar conclusions than the same paper. The authors are encouraged to compare their results to what would be achieved with local data, in order to really assess the added value of TROPOMI (and future missions)
3. Link to wetland and peatland models
   
   The method used in the manuscript merely deduce correlations between concentrations and environmental factors. The ML methods are very powerful in finding features and patterns, but often do not bring valuable insight on underlying processes. The  team behind the manuscript run their own process-based model to simulate methane emissions. Analyzing the links between direct conclusions from the present work and what a process-based model manages to simulate would bring valuable conclusions on what should be improved in models to better represent Northern methane emissions.