Abstract: Accurate monitoring of near-surface carbon dioxide (CO₂) concentrations is critical for assessing anthropogenic carbon emissions and formulating effective mitigation strategies. Current passive satellite remote sensing is limited by its susceptibility to aerosol and cloud interference and its restriction to providing only column-averaged dry air mole fractions (XCO₂). These limitations hinder the precise capture of near-surface carbon source and sink information. To address this, this study leverages the vertical profiling capabilities of spaceborne active Light Detection and Ranging (LIDAR) to investigate both above-cloud and below-cloud CO₂ concentrations. Utilizing observational data from the Aerosol and Carbon Detection LIDAR (ACDL) onboard the Atmospheric Environment Monitoring Satellite (DQ-1) in 2023, we developed a sub-cloud CO₂ signal separation model based on the Integrated Path Differential Absorption (IPDA) principle. This approach enabled the effective separation of above-cloud and below-cloud CO₂ signals over China. Subsequently, by integrating data from the Waliguan atmospheric baseline station and nighttime light imagery, we systematically analyzed the spatiotemporal distribution, seasonal variations, and diurnal differences of above-cloud and below-cloud XCO₂ across China for the year 2023. The analysis indicates that: (1) Above-cloud XCO₂ is minimally affected by surface perturbations and exhibits stable temporal variations, reliably reflecting the long-term background atmospheric concentration; (2) Below-cloud XCO₂ demonstrates a distinct spatial gradient with higher concentrations in the east and lower in the west, along with seasonal oscillations characterized by summer minima and winter maxima. The monthly average concentration peaked in April (~425.4 ppm) and reached a trough in August (~419.2 ppm). Additionally, a pronounced diurnal disparity was observed, with nighttime concentrations exceeding daytime levels by an average of 0.7 ppm; (3) Below-cloud XCO₂ exhibits a significant positive bias compared to total column concentrations, averaging 9.1 ppm higher. Furthermore, areas of high concentration show high spatial consistency with major urban agglomerations (e.g., Beijing-Tianjin-Hebei and the Yangtze River Delta) and regions with high nighttime light intensity. Spaceborne active LIDAR possesses a unique advantage in vertical profiling, effectively isolating background signals from the middle and upper atmosphere. The retrieved below-cloud XCO₂ more precisely captures near-surface anthropogenic carbon emissions and the dynamics of regional carbon sources and sinks, providing a more direct scientific basis for regional carbon budget assessments.