AbstractCoal mining ranks as the largest anthropogenic CH₄ source in China with emission factors (EFs) varying up to 30‐fold among inventories when applied to different provinces. The lack of independent evaluation of coal mining CH₄ EFs in China is one of the main uncertainties in estimating national total CH₄ emissions. Shanxi province, which supplies 25% of the national coal production, is the largest coal mining CH₄ emission region in China and even among the world's largest coal production regions. This area is also a significant anthropogenic CO₂ source because of high‐density power and industrial activities. Given the large uncertainties in CH₄ and CO₂ inventories from provincial to city scales, questions remain whether state‐of‐the‐art inventories have accurately estimated these emission hotspots. Here, we evaluate CH₄ and CO₂ emissions from one of the world's largest coal production regions near Taiyuan City, the capital of Shanxi province, China. CH₄ and CO₂ concentrations were measured from March 2018 to February 2019 from a 30‐m tower. These data were used within an inverse modeling framework to simulate both CH₄ and CO₂ concentrations and to evaluate EFs for this region. Results show generally good agreement between observed and simulated CH₄ concentrations. However, the CO₂ simulations were much lower compared to the observations. Given the minor role of NEE‐induced CO₂ enhancements, we believe that the large difference is attributed to the underestimation of anthropogenic CO₂ emissions. In general, the derived posteriori anthropogenic CH₄ emissions were 85.2(±18.1)% of a priori emissions, where fugitive CH₄ from coal mining accounted for ∼92.7% of total anthropogenic emissions. The derived coal mining EF was 23.2(±4.9) m³ CH₄/ton coal, close to the default value of high CH₄‐content coal, but twofold the province average that were reported by previous observation‐based studies in Shanxi province, indicating large spatial inhomogeneity in the coal mining CH₄ EF. The posteriori CO₂ emissions were 1.6‐fold of the a priori emissions, highlighting underestimation of CO₂ emissions in industrial cities and some potential large emission sources that are missing from state‐of‐the‐art inventories. Finally, we also emphasize the use of satellite observations and denser tower‐based networks are essential in resolving the spatial inhomogeneity of greenhouse gas emissions.