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Abstract. Methane (CH4), the second most important anthropogenic greenhouse gas, significantly impacts global warming. As the world's largest anthropogenic CH4 emitter, China faces challenges in accurately estimating its emissions. Top-down methods often suffer from coarse resolution, limited data constraints, and result discrepancies. Here, we developed the Regional Methane Assimilation System (RegGCAS-CH4) based on the WRF-CMAQ model and the EnKF algorithm. By assimilating extensive TROPOMI column-averaged dry CH4 mixing ratio (XCH4) retrievals, we conducted high-resolution nested inversions to quantify daily CH4 emissions across China, with a focus on Shanxi Province in 2022. Nationally, posterior CH4 emissions were 45.1 ± 3.8 TgCH4 yr−1, 36.5 % lower than the EDGAR estimates, with the largest reductions in the coal and waste sectors. In North China, emissions decreased most significantly, mainly attributed to the coal and enteric fermentation sectors. Posterior emissions in coal-reliant Shanxi Province decreased by 46.3 %. Sporadic emission increases were detected in major coal-producing cities but were missed by the coarse-resolution inversion. Monthly emissions exhibited a winter-low, summer-high pattern, with the rice cultivation and waste sectors showing higher seasonal increases than those in EDGAR. The inversion significantly improved XCH4 and surface CH4 concentration simulations, reducing emission uncertainty. Compared to other bottom-up/top-down estimates, our results were the lowest, primarily because the high-resolution inversion better captured local emission hotspots. Sensitivity tests underscored the importance of nested inversions in reducing the influence of boundary condition uncertainties on emission estimates. This study provides robust CH4 emission estimates for China, crucial for understanding the CH4 budget and informing climate mitigation strategies.
Feng et al. (Fri,) studied this question.