The multisource satellite observation data have been widely used in carbon cycle research owing to their long-term and large-scale characteristics. However, the sparse sampling density of satellite observation data often results in incomplete spatiotemporal coverage at certain time intervals, which hinders the accurate representation of global carbon dioxide (CO2) concentration variations and is inadequate for supporting research applications with different precision requirements. To address this issue, a new multiscale fixed rank kriging is proposed to generate long-term daily scale column-averaged dry-air mole fraction of CO2 (XCO2) products from 2016 to 2019 over the globe on grids of 1°, for which the XCO2 data from Orbiting Carbon Observatory-2, Orbiting Carbon Observatory-3, and Greenhouse gases Observing SATellite are applied. Experimental results show that the dataset has a high spatiotemporal resolution and coverage validated by the Total Carbon Column Observing Network data to effectively fill gaps in satellite observation data, with cross-validation of R2=0.93 and root mean square error = 1.06 ppm. Moreover, we analyze the spatial distribution and seasonal variation characteristics of global and Chinese XCO2 from 2016 to 2019, with XCO2 presenting an obvious latitudinal gradient and seasonal periodicity in space. The proposed method establishes a foundational research dataset for the analysis of spatiotemporal variation characteristics of CO2 concentration at global and regional scales, as well as investigations on carbon sources and sink.