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Observations of atmospheric carbon dioxide (CO) from satellites offer new data sources to understand global carbon cycling. The correlation structure of satellite-observed CO can be analyzed and modeled by geostatistical methods, and CO values at unsampled locations can be predicted with a correlation model. Conventional geostatistical analysis only investigates the spatial correlation of CO, and does not consider temporal variation in the satellite-observed CO data. In this paper, a spatiotemporal geostatistical method that incorporates temporal variability is implemented and assessed for analyzing the spatiotemporal correlation structure and prediction of monthly CO in China. The spatiotemporal correlation is estimated and modeled by a product-sum variogram model with a global nugget component. The variogram result indicates a significant degree of temporal correlation within satellite-observed CO data sets in China. Prediction of monthly CO using the spatiotemporal variogram model and space-time kriging procedure is implemented. The prediction is compared with a spatial-only geostatistical prediction approach using a cross-validation technique. The spatiotemporal approach gives better results, with higher correlation coefficient ( r), and less mean absolute prediction error and root mean square error. Moreover, the monthly mapping result generated from the spatiotemporal approach has less prediction uncertainty and more detailed spatial variation of CO than those from the spatial-only approach. [ABSTRACT FROM AUTHOR]