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China has made remarkable progress in controlling particulate matter, while O₃ pollution over China has become increasingly severe in recent years according to ground observations. Continuous monitoring of dynamic changes in O₃ concentrations on regional and national scales can provide valuable insights for pollution control policies. Therefore, an improved similarity distance-based space-time random forest (SDSTRF) model was developed to estimate the near-surface O₃ concentration using the surface measurements, satellite O₃ precursors, meteorological variables, and other auxiliary information. The O₃ concentration data over China were generated based on the developed model with a spatial resolution of 10 km and a temporal resolution of 1 day from 2016 to 2022. The validation results against the ground measurements indicate that the developed SDSTRF model effectively captures O₃ variations, achieving a coefficient of determination of 0.83 and a root mean square error of 20.37 μg/m³. The spatiotemporal variations of O₃ concentrations were investigated using the generated dataset. A significant increasing trend of 1.243 μg/m³/yr in O₃ concentrations was observed in eastern China during the COVID-19 pandemic, which was attributed to changes in NO_x concentrations. In this study, the possible reasons for the increase in O₃ concentrations are also discussed. Overall, the improved SDSTRF model and the comprehensive analysis of the spatiotemporal variations of near-surface O₃ will significantly contribute to achieving clean air in China.