Global anthropogenic XCO2 enhancements response to temperature changes with country-specific adaptability and sensitivity

Human heating- and cooling-induced CO₂ emissions exhibit substantially different responses to extreme temperatures across countries, leading to satellite-detectable enhancements in atmospheric concentration. Quantifying this relationship is crucial for understanding human-climate interactions and informing targeted carbon mitigation policies. However, a global, systematic assessment has been hindered by the lack of timely, reliable, high-resolution carbon monitoring data. Satellite observations of the column-averaged dry-air mole fraction of CO₂ (XCO₂) always suffer from cloud-induced data gaps and overwhelming interference from natural fluxes, impeding anthropogenic signal extraction. Here, we integrate OCO-2/3 XCO₂ and TROPOMI NO₂ observations via machine learning to reconstruct global daily 0.1° XCO₂ fields (2021-2023), and use local Moran’s I statistics to isolate anthropogenic XCO₂ enhancements (XCO_2en). The resulting XCO_2en data exhibit distinct seasonal cycles across the world’s top-emitting regions, characterized by elevated levels in summer and winter. SHapley Additive exPlanations (SHAP) analysis reveals a consistent V-shaped relationship globally between temperature and its contribution to XCO₂, indicating that both high and low temperature extremes elevate the SHAP value, with an optimal temperature (T_o) yielding the minimum value. Notably, despite vast differences in national temperature distributions, the T_o across countries converges around 21°C, suggesting a common human thermal adaptation threshold. Moreover, the slope of the V-shaped curve, representing the sensitivity of the XCO_2en response to temperature, exhibits a significant positive correlation with national GDP per capita (R=0.50, p<0.01) and a negative correlation with the share of renewable energy consumption (R=-0.34, p<0.05). Our model effectively delineates the spatiotemporal patterns of anthropogenic XCO_2en by leveraging carbon-nitrogen synergy, providing critical insights for decarbonization strategies and renewable energy transitions in diverse economies under carbon neutrality goals.