## 1 Introduction  
Nowadays, global climate change is profoundly affecting both natural and human systems [1, 2]. In 2023, the sixth Synthesis Report of the Intergovernmental Panel on Climate Change (IPCC) stated that human activities are undoubtedly contributing to global warming through the emissions of greenhouse gases (GHGs) [3]. Carbon dioxide (CO2), which has the highest concentration and broadest sources, exhibits pronounced absorption characteristics for solar visible light and infrared radiation from Earth [4], as well as the close association with biological activities, including human activities, establishes it as the predominant GHG [5]. There are limited ways to determine the CO2 concentrations in the atmosphere. Ground-based observation networks, such as the Network for the Detection of Atmospheric Composition Change (NDACC) and the Total Carbon Column Observing Network (TCCON), can obtain high-precision, ground-based measurements of GHGs [6, 7]. However, the high construction and operating costs of these networks and the strict site selection have led to its uneven global geospatial distribution, making it difficult to obtain continuous spatial distribution of CO2 concentration in large regions. Furthermore, atmospheric physicochemical models (such as CESM, WRF-Chem, CTMs, etc.) can be used to simulate and predict the transport, diffusion, and concentration distribution of various components,including atmospheric CO\({}_{2}\). However, the calculation of such models involves many physical and chemical processes, requiring large amounts of computational resources and data support, leading to inherent uncertainties [8; 9]. Remote sensing data possess the advantages of consistency, stability, and objectivity. Envisat-1, launched by the European Space Agency in 2002, carried the Scanning Imaging Absorption Spectrometer for Atmospheric Chatrography (SCIAMACHY) sensor for atmospheric mapping and was the first satellite that had the ability to measure atmospheric CO\({}_{2}\) in the world. This mission confirmed the feasibility of space-based detection of near-surface CO\({}_{2}\) concentrations in the near-infrared spectral range [10]. After that, GHG satellite GOSAT, as well as carbon observation satellites such as OCO-2, OCO-3, and TanSat, acquire the column-averaged dry air mole fraction of CO\({}_{2}\)--XCO2 by detecting short-wave infrared reflected from the Earth's surface and thermal infrared emitted by the surface and atmosphere [11]. Bias correction of these raw XCO2 retrievals, including the removal of footprint bias, the removal of XCO2 errors correlated with specific variables, and the determination of global offsets based on the \"truth proxy\" (TCCON), allows users to obtain the best estimated XCO2 L2 products directly. Thus, satellite observations have become one of the most reliable means currently available for capturing changes in global and regional atmospheric CO\({}_{2}\) concentration [12; 13]. This remote sensing method does not rely on numerical model simulations and can provide independent atmospheric CO\({}_{2}\) concentration from space observation, which can address the sparsity problem of the GHG monitoring network. However, due to factors such as clouds and aerosols and limitations in inversion algorithms, the effective retrieval of XCO2 data is temporally and spatially discontinuous, which significantly constrains the widespread application of satellite observations. Figure S1 in the Supplementary Materials illustrates the XCO2 footprint density of OCO-2 satellite over mainland China at daily, weekly, monthly, and yearly time scales, from which it can be seen that the OCO-2 XCO2 data have the noticeable problem of missing data at various temporal scales. Therefore, filling the spatial and temporal gaps in XCO2 data to obtain high-precision and high-resolution full-coverage maps is significant for enhancing the application potential of XCO2 satellite data and understanding the spatiotemporal distributions and patterns of atmospheric CO\({}_{2}\) on a large scale.