In Gaubert et al., the authors use multiple simulations of the CESM model, with different meteorology and emissions, to understand relationships between OH and the methane growth rate, as well as the drivers of interannual variability in OH. The paper is suitable for publication in ACP once the below comments are addressed:

General Comments:

The paper could be strengthened greatly by re-writing for clarity. The enumerated main conclusions in the final section are important and definitely supported by the work, but I’m not sure I would have realized those were the major points you were trying to emphasize. I would consider reducing some unnecessary text (there are many paragraphs that are simply repeating numbers in tables and figures) and reorganizing some paragraphs (e.g., the paragraph starting in line 418 is about 1.5 pages in length) to try to improve clarity. There are also, not infrequently, grammatical errors that need to be corrected

Your focus is primarily on globally averaged statistics, which for CH4 is appropriate, but for CO, which is much shorter lived, and the OH budget, which can vary substantially in the horizontal and vertical, some discussion of their spatial heterogeneity is warranted. How does the relative importance of the different OH budget terms vary spatially? Does looking at your results from a more regional perspective change the results in Figures 10 & 8 significantly?

Specific Comments:

Figure 1 caption: I assume the text at the end starting with “At the bottom right,…” is from a previous version of the figure. If not, your meaning here is unclear and the text should be changed.

Line 163: should be “emissions from vegetation are estimated…” not “is estimated”.

Figure 2, panel g: Some discussion about the cause of the dramatic difference in lightning NOx between the ERA and MERRA2 datasets starting in 2020 is warranted, particularly given results you discuss in Section 6. Do you know what caused the sudden drop in the ERA emissions? Do you have any indication which is likely to be correct?

Section 2.2: It would also be worthwhile to try and evaluate the accuracy of your modeled OH, since you talk about the OH budget later on and any extrapolations about the impacts of OH on CO and CH4 in the actual atmosphere depend on your modeled OH accuracy. While obviously a more difficult comparison given the diurnal variability in OH, at the very least, showing that your modeled spatial and temporal variability agree with in situ observations ( e.g. from ATom or KORUS-AQ) or inversion derived trends within reason would further strengthen your argument.

Figure 4: I'm not sure the coloring of your boxes is particularly helpful. I found it somewhat confusing. Maybe you could just consider, putting in bold, values above/below a certain threshold?

Section 3.2: Is there any spatial or temporal variability in these statistics? Numbers averaged globally can hide all sorts of issues, particularly with something like bias where you can have areas of large negative and positive bias that cancel each other out. Adding a map or two would be helpful (even as a supplementary figure).

Line 330: “Despite different OH…”. It would be useful to have maps showing the climatological mean OH for your different simulations so readers can understand the spatial variability in OH in the different models. The spatial variability, while potentially not very important for CH4 given its long lifetime, would be important for CO, which has a much shorter lifetime.

Line 418: Sentence starting with “Tropospheric OH generally follows…” needs a citation.

Line 427: “…, confirming that interannual variability is controlled almost entirely by OH chemistry”. Do you mean IAV in the loss of CH4 or in the CH4 growth rate?

Line 469: Does this methodology take into account cross-correlations between the different terms, for example surface NOx and anthropogenic CO emissions? If not, how will that affect your conclusions, as I don’t think it would be appropriate to add the various contributions since they aren’t independent.

This is an interesting, well written study examining the extent to which OH variability influences methane growth rate over 2003-2024 and the underlying drivers of the OH variability. Gaubert et al. run 8 different model simulations, with different setups for methane (prescribed vs. interactive), emissions of other reactive gases and aerosols, scaling’s of CO emissions, and meteorological nudging datasets. They examine how the year-on-year change in OH relates to the year-on-year methane change and the drivers of this OH change.

Changing the emission inventories (10%) and changing the meteorological nudging datasets (8%) had the largest impacts on OH.

Overall, this paper presents a useful addition to the field with a rigorous evaluation against observations and well explained decomposition of the drivers, and I would encourage its acceptance once the authors have responded to my comments below.

Major comments

The meteorology plays an important role, via its impact on BVOC emissions and lightning NO_x. Can the authors give any indication as to which re-analysis dataset is considered more accurate, particularly in relation to the variables in the locations which affect the aforementioned processes?

I also note that the emissions of isoprene quoted are on the high end of current estimates (e.g. 389 TgC/yr from CAMS-GLOB-BIO v3.1 Sindelarova et al., 2022) and this should be noted.

My understanding is that Mozart T1 schemes already includes quite comprehensive isoprene chemistry, as well as the newly added SLH mentioned by the authors. Could the authors comment further on how the T1 scheme differs from the MOM scheme in the context of OH recycling?

The CT-CH4-2025 emissions are themselves derived assuming a prescribed OH field, as the authors note. How does this affects the claim that the methane emissions are not a major driver of OH variability?

I appreciate single model studies allow the researchers to examine the response in more detail compared to multi-model studies where there are fewer common variables between models. Nevertheless, it would be good for the reviewers to highlight that the results presented here are from a single model with a single chemical scheme and that comparable experiments in other models would be useful in assessing the robustness of the findings

Minor points

 In figure 3, as panels (a)-(f) relate to a range of gases, please change legend’s black line to read “observations” not “CO (ppb)”.

In figures 10 and 11, why in places is the net line clear from the individual contributions (e.g., 2006 in panel (d)). I would have thought that, at most, I could sit on the maximum of the stacked contributions.

What are the C1 and C2 VOCs in Figure 10?