Aiming at existing problems in current O₃ single model forecast, an efficient superensemble forecast based on running active range (AR-SUP) is proposed and applied to the EMS-YRD (multi-model ensemble air quality forecast system for the Yangtze River Delta) O₃ forecast during the study period in 2015. The performance of the newly proposed method is compared with those of R-SUP (Running Training Period Superensemble), EMN (Ensemble Mean), and BREM (Bias-Removed Ensemble Mean). The results show that compared with the other three ensemble methods, the AR-SUP exhibits significant improvement in daily O₃ forecast with the RMSE reduced by 20% and 23% from that of the best single model in cool and warm seasons respectively. Further application of the AR-SUP in O₃ ensemble forecast also shows high forecasting skills when the predicting time is extended to 48 h and 72 h. A number of statistical measures (i.e., reduced errors, increased correlation coefficients, and index of agreement) show that the forecasting skill has been improved at all the locations within the study region during all seasons, which indicates this method can be used to help improve the accuracy and reliability of short-term forecasts.