Mapping high-resolution monthly XCO ₂ across China and its relationship with human activities

Abstract Monitoring atmospheric CO 2 concentration changes across China is crucial for reducing carbon emissions and tackling global warming. Due to monitoring gap in satellite observations, this paper uses the Extremely Randomized Trees (ERT) model alongside OCO-2 satellite data and predictor variables to develop a 0.1° high-resolution monthly dataset of column-averaged dry air CO 2 mole fraction (XCO 2 ) in China between 2015 and 2023. The model demonstrates high precision, achieving a sample cross-validation with R 2 of 0.988 and RMSE of 0.732 ppm, while validation at Hefei and Xianghe stations yielded R 2 (RMSE) of 0.951 (1.339 ppm) and 0.865 (1.840 ppm), respectively. Therefore, this dataset is suitable for high-resolution studies. The results show that XCO 2 across China is increasing at a rate of 2.46 ppmyr −1 during 2015–2023, with XCO 2 highest in spring and lowest in summer. There is a significant spatial heterogeneity in XCO 2 distribution during the period, characterized by a gradient of ‘higher in the east and lower in the west’. Among the factors analyzed, the most influential were population density, transportation infrastructure level and energy intensity, which emerged as the strongest human activity-related drivers of XCO 2 variability, with population density exhibiting a positive correlation with XCO 2 , while the latter two show a negative correlation with XCO 2 . This paper can provide a scientific basis for policymaking to reduce carbon emissions, promote sustainable development and address climate change.