Atmospheric particulate pollution in China’s megacities has heightened public concern over air quality, highlighting the need for precise identification of urban pollution characteristics and pollutant transport mechanisms to enable effective control and mitigation. In this study, a new method combing the High Accuracy Surface Modeling (HASM) and Multiscale Geographically Weighted Regression (MGWR) was proposed to derive seasonal high spatial resolution PM2.5 concentrations. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) was applied to analyze the seasonal spatial variations, transport pathways, and potential sources of PM2.5 concentrations across China’s four megacities: Beijing, Shanghai, Xi’an, and Chengdu. The result indicates that: (1) the proposed method outperformed Kriging, inverse distance weighting (IDW), and HASM, with coefficient of determination values ranging from 0.91 to 0.94, and root mean square error values ranging from 1.98 to 2.43 µg/m3, respectively; (2) all cities show a similar seasonal pattern, with PM2.5 concentrations highest in winter, followed by spring, autumn, and summer; Beijing has higher concentrations in the south, Shanghai and Xi’an in the west, and Chengdu in central urban areas, decreasing toward the rural area; (3) potential source contribution function and concentration weighted trajectory analysis indicate that Beijing’s main potential PM2.5 sources are in Hebei Province (during winter, spring, and autumn), Shanghai’s are in the Yellow Sea and the East China Sea, Xi’an’s are in Southern Shaanxi Province, and Chengdu’s are in Northeastern and Southern Sichuan Province, with all cities experiencing higher impacts in winter; (4) there is a negative correlation between precipitation, air temperature, and seasonal PM2.5 levels, with anthropogenic emissions sources such as industry combustion, power plants, residential combustion, and transportation significantly impact on seasonal PM2.5 pollution.
