The manuscript, "Reporting of Gridded (0.1°×0.1°) Methane Emission Data for India to Redefine Global Climate Studies" by Mishra et al., addresses the important and timely topic of quantifying and spatially representing methane (CH₄) emissions across India. Methane plays a significant role as a climate forcing gas, and there are large uncertainties in regional CH₄ budgets, especially in developing countries. Thus, this study is highly relevant for improving emission inventories and supporting the Global Methane Pledge and the Global Greenhouse Gas Watch (G3W) frameworks. The authors attempted to present a comprehensive, 0.1°×0.1° gridded inventory for 2023 that incorporates both anthropogenic and natural sources, as outlined in IPCC guidelines. However, despite the significance of the topic, the manuscript, in its current form, requires substantial revision to improve methodological transparency, contextual integration, and scientific robustness.

Major comments:

The main concern is the limited transparency of the methodology used to derive the 0.1°×0.1° gridded emissions. Although equations (1–15) outline a general framework, the description lacks sufficient detail to understand how the spatial allocation of emissions was implemented. It is unclear which proxy datasets were used to distribute emissions geographically, how high-resolution input data were obtained for different source sectors, and if region-specific adjustments or scaling procedures were applied. To ensure reproducibility and allow for a meaningful evaluation of the results, the manuscript must provide a thorough description of the data sources, spatial disaggregation methods, emission factors, and activity data used for each category (e.g., livestock, rice paddies, fossil fuels, waste management).

The methodology for uncertainty quantification is unclear and needs substantial improvement. Figure 5, which shows sector-wise uncertainty estimates, is difficult to interpret. The reported total uncertainty of ±59% is unusually large compared to other inventories and requires justification. The authors should clearly describe their approach to uncertainty estimation and specify how they defined, combined, and propagated uncertainties in emission factors and activity data across sectors. They should also provide details on assumed distributions and aggregation procedures to ensure reproducibility. Figure 5 should be revised and accompanied by a concise discussion that identifies the major contributors to the total uncertainty and explains why it is so high compared to other studies.

This study attempted to present a valuable dataset that could help fill a significant gap in methane emission information for India. The inclusion of 25 source categories, as well as the effort to quantify emissions from natural sources such as wetlands and termites, demonstrates the study's comprehensive scope and is commendable. However, the methodology is described only superficially, making it difficult to assess the reproducibility and robustness of the results. The comparison with previous studies is limited and omits several key references, which weakens the scientific context and credibility of the analysis. Furthermore, Figure 4, intended to support the intercomparison, is confusing because it includes unrealistically low total estimates derived from studies considering only a limited subset of emission sectors. A clearer, more balanced comparison that includes comprehensive inventories would significantly strengthen the manuscript.

Section 3.3 largely overlaps with the introduction and does not offer new insights. The intercomparison should focus on a quantitative evaluation against prior bottom-up and top-down estimates. Several key studies are missing from the literature review and CH₄ budget comparison, including (Miller et al. 2019; Ganesan et al. 2017; Chandra et al. 2021), as well as the updated Global Methane Budget (Saunois et al. 2024).

In conclusion, while this paper tries to address a critical gap in regional methane emission data, the methodological details, intercomparison rigor, and literature integration must be substantially improved. With clearer explanations of the gridding approach and inclusion of the missing references, as well as stronger data analysis, the manuscript could become a valuable contribution to CH₄ emission research and policymaking. At this stage, the recommendation is a major revision.

Minor comments:

1. Fig. 1b: The labels on the y-axis are not clearly defined.
2. Fig. 2: The axis and color bar labels should be enlarged.
3. Lines 277-278: " The methane footprint of Indians is found to be 27 kg and the per square km area is attributed to 11.6 tonnes." This sentence is confusing and should be revised.
4. Fig. 4: Why are some estimations so low? Apparently, some of the works considered here include only separate categories and do not provide good total estimations.

References:

Chandra, N., P. K. Patra, J. S. H. Bisht, and A. Ito. 2021. “Emissions from the Oil and Gas Sectors, Coal Mining and Ruminant Farming Drive Methane Growth over the Past Three Decades.” Journal of The. https://www.jstage.jst.go.jp/article/jmsj/99/2/99_2021-015/_article/-char/ja/.

Ganesan, Anita L., Matt Rigby, Mark F. Lunt, Robert J. Parker, Hartmut Boesch, N. Goulding, Taku Umezawa, et al. 2017. “Atmospheric Observations Show Accurate Reporting and Little Growth in India’s Methane Emissions.” Nature Communications 8 (1): 836.

Miller, Scot M., Anna M. Michalak, Robert G. Detmers, Otto P. Hasekamp, Lori M. P. Bruhwiler, and Stefan Schwietzke. 2019. “China’s Coal Mine Methane Regulations Have Not Curbed Growing Emissions.” Nature Communications 10 (1): 1–8.

Saunois, Marielle, Adrien Martinez, Benjamin Poulter, Zhen Zhang, Peter Raymond, Pierre Regnier, Joseph G. Canadell, et al. 2024. “Global Methane Budget 2000–2020.” https://doi.org/10.5194/essd-2024-115.

Authors have prepared a regional  high-resolution gridded methane emission data for 2023 using different cities observation dataset. They have developed different sectoral methane emission over India, which will help decision  and policy makers from private and public sector.  Indi-Gangetic plane has the highest methane emission and the reasons are understandable. Authors conclude that their method and dataset rectified issue with EDAGR 2023 dataset. Figure 4 is a nice 3D plot to show the difference of earlier studies as compared to their study. They concluded not to ignore the natural methane emission sources like wetlands and solid waste, alongwith all known emmiters like livestock and agriculture. This study will add knowledge to the emmission repository for future studies and necessarty climate actions. This manuscript presents a timely and policy‑relevant high‑resolution methane inventory representing Indian methane emission, a major contributor to global CH4 emissions. Study integrated 25 anthropogenic and natural sources for recent year 2023 is much needed emission work over India and it substantially improves spatial pattern of CH4 emission.  The paper is well written and discussed in an elaborative way, although need some technical corrections.

1. Title is misleading as if it is for multiyear reanalyses dataset. Suggested to add the year "2023".
2. Methodology mainly refers to supplimentary material. In stead authors could mention few important pointers like usage of GIS and associated datasets.
3. page 15 line number 323 :  1.8Tg, per yr is missing.
4. Line number 376 : T is missing from This.
5. For wetlands and termites, uncertainty is very large (±137% and ±161%). Briefly describe the sources of such high uncertainties and discuss how this affects the robustness of spatial hotspot identification.
6. Since the paper already cites Janardanan et al. (2024), briefly discuss whether your national totals fall within the range of inversion derived national estimates including this and other such studies.
7. In the abstract, it is mentioned, “However, surprisingly cities exhibit lower CH4 as compared to other semi-urban/rural regions,” which is asserted without explanation. Give some explanation in results and discussion section.
8. Lines 51: “Keeping the diversity of methane sources across the world…”, Suggestion: “Given the diversity of methane sources”.
9.  Line 64: “However, India has not signed the ‘Global Methane Pledge’…”. Consider briefly clarifying why this is relevant to your inventory.
10. Line 72–74: “Although some global inventories have reported CH4 emissions from India… they primarily relied on Tier I top-down approach, resulting in erratic estimations…”. Suggestion: “Erratic” is quite strong and a bit informal. So, I suggest rewriting the sentence.
11. Line 150: “Wetlands have emerged as the second largest source of natural source of CH4 emissions in India…”. Remove repetitive words like ‘source’ and check for others in the entire manuscript.
12. Line 267–269: “Cremation, a practice predominantly associated with the Hindu religion, is another unorganized source…”. Suggestion: You might slightly neutralize the tone by focusing on practice rather than religion: “Traditional cremation practices are another unorganized source of methane emissions…”.
13. Line 375–376: “his newly developed state-of-the-art…” Typing error: “This newly developed state-of-the-art…”. Search the entire manuscript for such typing mistakes.
14. Ensure consistent use of “Tg/yr” and percent symbols and avoid ambiguity between “percent of total India” and “percent of sectoral total”.