Based on the ground-level observed NO₂ concentration, satellite-observed NO₂ column concentration from the Ozone Monitoring Instrument (OMI) and meteorological parameters, we comprehensively consider the seasonal and regional differences in the relationship between NO₂ column concentration and measured NO₂ concentration and establish a two-stage combined ground NO₂ concentration estimation (TSCE-NO₂) model using a support vector machine for regression (SVR) and a genetic algorithm optimized back propagation neural network (GABP). On this basis, the spatial-temporal variation in the modelled ground-level NO₂ concentration over China during the period of 2005–2019 was analysed. The results show that the TSCE-NO₂ model proposed in this study provides a reliable estimation of the modelled ground-level NO₂ concentration over China, effectively filling the spatial and temporal gaps in China's air quality ground monitoring network (the model's correlation coefficient, R, is 0.92, the mean absolute error, MAE, is 3.62 μg/m³, the mean square percentage error, MSPE, is 0.72%, and the root-mean-square error, RMSE, is 5.93 μg/m³). The analysis results of the spatial and temporal variation indicate that (1) the perennial ground-level NO₂ concentration over China is high in the eastern area and low in the western area, and the high values are mainly distributed along the northern coast, the eastern coast, the middle reaches of the Yangtze River, the middle reaches of the Yellow River, the Pearl River Delta and the Sichuan Basin. (2) The modelled ground-level NO₂ concentrations over China are highest in winter, followed by those in autumn and spring, and they are lowest in summer. Before 2011, the ground-level NO₂ concentration over China increased at a rate of 0.348 ± 0.132 μg/(m³∙a) but decreased at a rate of 0.312 ± 0.188 μg/(m³∙a) after 2011. (3) From 2011 to 2019, measures such as energy savings and emission reductions alleviated NO₂ pollution on the premise of ensuring sustained China's GDP growth.