Deriving Full‐Coverage and Fine‐Scale XCO 2 Across China Based on OCO‐2 Satellite Retrievals and CarbonTracker Output

Due to the coarse spatial resolution, the column‐averaged dry‐air mole fraction of CO 2 (XCO 2 ) data from the CarbonTracker may be inadequate to reflect the spatial heterogeneity of XCO 2 . We developed a machine learning model to fill the data gaps in the Orbiting Carbon Observatory 2 satellite retrievals across China during 2015–2018, with cross‐validation R 2 = 0.95 and RMSE = 0.91 ppm. Based on the gap‐filled data set, the multiyear average XCO 2 was the highest in East China (405.71 ± 3.72 ppm) and the lowest in Northwest China (403.99 ± 3.47 ppm). At the national level, the multiyear seasonal XCO 2 varied from 402.54 ± 3.95 ppm in summer to 406.28 ± 3.19 ppm in spring. While the XCO 2 kept increasing, the rate of increase declined from 3.23 to 2.10 ppm/year. The machine learning approach is feasible for downscaling and calibrating the CarbonTracker XCO 2 data. The full‐coverage and fine‐scale XCO 2 data set is expected to advance our understanding of the carbon cycles. As the most abundant greenhouse gas, atmospheric CO 2 is considered one of the main contributors to climate change. Climate research requires comprehensive spatiotemporal distributions of CO 2 . However, the sparseness of the ground‐based measurements and lack of a fine‐scale modeled CO 2 data set limits our understanding of the carbon dynamics. We developed a machine learning model to fill the data gaps in the satellite retrievals of the column‐averaged dry‐air mole fraction of CO 2 (XCO 2 ) for China during 2015–2018. The XCO 2 levels were the highest in East China and the lowest in Northwest China, and were the lowest in summer and the highest in spring. While the XCO 2 levels kept increasing, the rate of increase slightly declined. The full‐coverage and fine‐scale XCO 2 data are valuable for policy making in carbon emission management. This work affirms the necessity of modeling the XCO 2 from satellites, which can advance our understanding of the carbon cycles. The machine learning model exhibited decent performance in predicting gridded daily XCO 2 , with cross‐validation R 2 = 0.95 The machine‐learning predicted XCO 2 revealed considerably higher spatial heterogeneity than the CarbonTracker output Based on the gap‐filled XCO 2 data set, the average XCO 2 during 2015–2018 was the highest in East China and the lowest in Northwest China