Validated Spatiotemporal Patterns and Drivers of XCH4 Concentrations East of the Hu Line in China (2010–2024)

Most studies on atmospheric methane (CH4) in China focus on national or provincial scales and short-term observations, while the long-term spatiotemporal characteristics and potential influencing factors of CH4 concentrations east of the Hu Line remain poorly understood. Understanding these patterns is essential for regional climate mitigation and management. Using GOSAT L3 satellite-derived XCH4 observations from 2010 to 2024, validated against Total Carbon Column Observing Network (TCCON) measurements from Hefei and Xianghe stations, this study integrated Pearson correlation analysis and the Geodetector model to characterize the spatiotemporal patterns of XCH4 and quantify the interactive effects of environmental and socioeconomic factors east of the Hu Line. (1) Monthly mean GOSAT XCH4 showed good agreement with TCCON observations at the Hefei and Xianghe sites, with coefficients of determination (R2) of 0.826 and 0.629 (p < 0.05). Annual mean XCH4 increased overall from 1.802 ppm in 2010 to 1.919 ppm in 2024, corresponding to a cumulative increase of approximately 6.5% and a compound annual growth rate of approximately 0.45%. Statistically significant increases were concentrated in central, southeastern, and parts of southwestern China, including the middle and lower Yangtze River Plain, the Sichuan Basin, and the eastern Yunnan–Guizhou Plateau. (2) The 2010–2024 climatological seasonal means showed higher XCH4 in southern than in northern regions in spring and winter, weaker spatial contrasts in summer, and higher values in central and southern regions in autumn. (3) The relationships between XCH4 and influencing factors showed clear regional heterogeneity. Hydrothermal variables exhibited stronger associations with XCH4 in major agricultural regions, such as the Northeast Plain and Central China agricultural transition region, whereas socioeconomic indicators showed more heterogeneous spatial patterns. The South China coastal region and low-value sparse region displayed distinct correlation patterns, reflecting regional differences in environmental and anthropogenic influences. (4) Geodetector results showed that temperature (q = 0.880) and precipitation (q = 0.681) were the dominant explanatory factors for XCH4 spatial differentiation. Anthropogenic activity indicators, including population density (q = 0.528), nighttime lights (q = 0.518), and GDP (q = 0.348), also exhibited considerable explanatory power. (5) Among the 36 factor interactions, 31 exhibited bivariate enhancement and 5 showed nonlinear enhancement. The strongest interactions were observed between EVI and temperature (q = 0.940), temperature and GDP (q = 0.934), and urbanization rate and temperature (q = 0.931). These findings provide a regional multi-factor basis for understanding XCH4 spatial heterogeneity and support further methane monitoring and differentiated mitigation strategies east of the Hu Line.