## 4 Discussion  
### Spatial Inhomogeneity of Accuracy  
Given the large spatial heterogeneity of urban structure, climatic conditions, vegetation, and other characteristics in the study area and the uneven distribution of satellite  
Figure 6: Comparisons between CNN-STK XCO2 and TCCON XCO2. (**a**–**c**) are validation results of the average CNN-STK XCO2 within circular geographic regions centered on TCCON sites, with diameters of 1\({}^{\circ}\), 3\({}^{\circ}\), and 5\({}^{\circ}\), respectively, compared against measurements from the Hefei site and Xianghe site of the TCCON network. (**d**) presents line plots of CNN-STK XCO2 and TCCON measurements within the 3\({}^{\circ}\) diameter geographic region, accompanied by a bar chart of their biases. observation data across different seasons and regions, there may be corresponding differences in the accuracy of CNN-STK XCO2 data. We separately calculated the _REs_ between the interpolations and observations for each season and plotted their spatial distributions (Figure 7) for specific evaluation. In spring, autumn, and winter, a similar pattern of error distribution is observed, with higher error points clustering in southern China, particularly in the eastern margin of the Qinghai-Tibet Plateau (highlighted by the blue box in Figure 7a), which marks the transition zone between two climatic regions (Figure 7d). Lower error points are concentrated in the green box area, situated in temperate arid/semi-arid regions and the western part of the Qinghai-Tibet Plateau. Based on the climatic zoning map, it is evident that southern China, heavily influenced by monsoons, has significantly fewer effective XCO2 data points collected by carbon observation satellites compared to northern regions. Especially in the subtropical humid region of the middle and lower reaches of the Yangtze River Plain, noticeable data gaps occur in spring, autumn, and winter, greatly impacting data reconstruction accuracy in this area. Summer exhibits a different distribution pattern. Figure 7b illustrates that high-error points are clustered in northeastern China, which boasts the largest natural forest area in China, accounting for approximately 37% of the national total forest area. Although summer is brief, characterized by abundant precipitation and high temperatures fostering vigorous vegetation activity, it serves as an important forest carbon sink region. Therefore, the data quality here is slightly poorer compared to other seasons, potentially influenced by vegetation photosynthesis-respiration dynamics. Additionally, apart from the northern marginal region of China, data scarcity prevails across most regions during this season, hindering the assessment of error value clustering. We further conducted statistical analysis on the data accuracy within areas of high and low XCO2 aggregation for each season, as shown in Figure 8, where the hot spot indicates regions of high-value aggregation. In contrast, cold spots represent areas of low-value aggregation. Details of the XCO2 aggregation method and distribution maps are provided in the Supplementary Materials Section S4. Within a 95% confidence interval, all four seasons exhibit significant clustering characteristics (Table S6). Figure S6 shows distinct patterns of hot and cold spots across different seasons in China. Specifically, in spring,  
Figure 7: Spatial distributions of relative errors (_REs_) of XCO2 for each season. In (**a,b**): Blue boxes show the high _RE_ areas, while green boxes show the low _RE_ areas. In (**d**): 1�  
autumn, and winter, the spatial distribution of hot and cold spots is generally similar, with hot spots widely distributed in the central and eastern regions, as well as the northeastern region. In contrast, cold spots are mainly concentrated in the western regions. In contrast, during summer, hot spots are predominantly located in southern and western China, while cold spots are evident in the northeastern region. The various clustering regions demonstrate relatively high accuracies across different seasons.