Accurate nationwide spatiotemporal distribution of ambient ozone product is critical for environment & health departments and for researches to investigate the influence of ozone for epidemiological studies. Our hybrid method, the novel CAMS (The Copernicus Atmosphere Monitoring Service) ozone improvement (CAO₃_I) method, is the first attempt to predict ambient ozone by improving CAMS ozone (CAO₃) products. For this novel framework, the SVM (Support Vector Machine) has been adopted for classification through the most significant regional ozone patterns which were extracted through the REOF (Rotated Empirical Orthogonal Function) technique. For each classified region, meteorological data, geographical data, CAMS ozone and ground-sites ozone are fed into random forest for regional regulation training and prediction. The CAO₃_I method has shown its great feasibility in daily ozone surface distribution prediction. Based on daily averaged ozone concentrations for each station (STO₃), the performance of CAO₃ products (R² = 0.35, RMSE = 25.77 μg/m³, MAPE = 42.06) have significantly improved to CAO₃_I (R² = 0.81, RMSE = 14.10 μg/m³, MAPE = 22.37), which shows above 97.4% R² and RMSE have been improved. Our model is also capable to predict high level ozone concentrations in summer where the R² has improved from 0.37 (for CAO₃) to 0.81. In comparison with ground monitoring stations, the CAMS ozone improvement results can excellently reflect the distribution of daily ground-level ozone concentration and outperform previous statistical models in predicting ambient O₃ concentrations. Therefore, the prediction results and our proposed model can be used for future epidemiological studies and air pollution controlling programs.