CO₂ is one of the most important greenhouse gases (GHGs) in the earth’s atmosphere. Since the industrial era, anthropogenic activities have emitted excessive quantities of GHGs into the atmosphere, resulting in climate warming since the 1950s and leading to an increased frequency of extreme weather and climate events. In 2020, China committed to striving for carbon neutrality by 2060. This commitment and China’s consequent actions will result in significant changes in global and regional anthropogenic carbon emissions and therefore require timely, comprehensive, and objective monitoring and verification support (MVS) systems. The MVS approach relies on the top-down assimilation and inversion of atmospheric CO₂ concentrations, as recommended by the Intergovernmental Panel on Climate Change (IPCC) Inventory Guidelines in 2019. However, the regional high-resolution assimilation and inversion method is still in its initial stage of development. Here, we have constructed an inverse system for carbon sources and sinks at the kilometer level by coupling proper orthogonal decomposition (POD) with four-dimensional variational (4DVar) data assimilation based on the weather research and forecasting–greenhouse gas (WRF–GHG) model. Our China Carbon Monitoring and Verification Support at the Regional level (CCMVS-R) system can continuously assimilate information on atmospheric CO₂ and other related information and realize the inversion of regional and local anthropogenic carbon emissions and natural terrestrial ecosystem carbon exchange. Atmospheric CO₂ data were collected from six ground-based monitoring sites in Shanxi to verify the inversion effect of regional anthropogenic carbon emissions by setting ideal and real experiments using a two-layer nesting method (at 27 km and 9 km). The uncertainty of the simulated atmospheric CO₂ decreased significantly, with a root-mean-square error of CO₂ concentration values between the ideal value and the simulated after assimilation was close to 0. The total anthropogenic carbon emissions in Shanxi Province in 2019 from the assimilated inversions were approximately 28.6% (17%–38%) higher than the mean of five emission inventories using the bottom-up method, showing that the top-down CCMVS-R system can obtain more comprehensive information on anthropogenic carbon emissions.