| Summary: | Methane (CH<sub>4</sub>) is one of the most important greenhouse gases causing the global warming effect. The mapping data of atmospheric CH<sub>4</sub> concentrations in space and time can help us better to understand the characteristics and driving factors of CH<sub>4</sub> variation as to support the actions of CH<sub>4</sub> emission reduction for preventing the continuous increase of atmospheric CH<sub>4</sub> concentrations. In this study, we applied a spatiotemporal geostatistical analysis and prediction to develop an approach to generate the mapping CH<sub>4</sub> dataset (Mapping-XCH<sub>4</sub>) in 1° grid and three days globally using column averaged dry air mole fraction of CH<sub>4</sub> (XCH<sub>4</sub>) data derived from observations of the Greenhouse Gases Observing Satellite (GOSAT) from April 2009 to April 2020. Cross-validation for the spatiotemporal geostatistical predictions showed better correlation coefficient of 0.97 and a mean absolute prediction error of 7.66 ppb. The standard deviation is 11.42 ppb when comparing the Mapping-XCH<sub>4</sub> data with the ground measurements from the total carbon column observing network (TCCON). Moreover, we assessed the performance of this Mapping-XCH<sub>4</sub> dataset by comparing with the XCH<sub>4</sub> simulations from the CarbonTracker model and primarily investigating the variations of XCH<sub>4</sub> from April 2009 to April 2020. The results showed that the mean annual increase in XCH<sub>4</sub> was 7.5 ppb/yr derived from Mapping-XCH<sub>4,</sub> which was slightly greater than 7.3 ppb/yr from the ground observational network during the past 10 years from 2010. XCH<sub>4</sub> is larger in South Asia and eastern China than in the other regions, which agrees with the XCH<sub>4</sub> simulations. The Mapping-XCH<sub>4</sub> shows a significant linear relationship and a correlation coefficient of determination (R<sup>2</sup>) of 0.66, with EDGAR emission inventories over Monsoon Asia. Moreover, we found that Mapping-XCH<sub>4</sub> could detect the reduction of XCH<sub>4</sub> in the period of lockdown from January to April 2020 in China, likely due to the COVID-19 pandemic. In conclusion, we can apply GOSAT observations over a long period from 2009 to 2020 to generate a spatiotemporally continuous dataset globally using geostatistical analysis. This long-term Mpping-XCH<sub>4</sub> dataset has great potential for understanding the spatiotemporal variations of CH<sub>4</sub> concentrations induced by natural processes and anthropogenic emissions at a global and regional scale.
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