Abstract: Taking the landfall typhoons "Nepartak", "Nida", "Meranti", and "Megi" in mainland China in 2016 as examples, this study evaluates the ensemble forecasts of typhoon track, landfall time, and landfall location using products from the China Meteorological Administration-Regional Ensemble Prediction System (CMA-REPS). On this basis, a new real-time optimal selection scheme for typhoon track ensemble forecasts, the 0～12 h Minimum Cumulative Track Error Scheme (MCTES), is proposed by combining operational real-time typhoon positioning data. This scheme is compared with the 12 h Minimum Track Error Scheme (MTES) to provide a reference for more effective operational applications of CMA-REPS typhoon track ensemble forecasts. The results show as follows. (1) Overall, in terms of typhoon track forecasting for landfalling typhoons, the ensemble mean forecast of CMA-REPS is inferior to the control forecast. (2) The MCTES real-time optimal typhoon track ensemble forecast selection scheme significantly improves the typhoon track results of both the CMA-REPS ensemble mean and control forecasts, outperforming the MTES scheme, CMA-REPS control forecast, and ensemble mean in sequence. Compared with the control forecast, the MCTES scheme reduced the average absolute errors in typhoon landing time, landing point, and the typhoon’s track movement every 12 h within the 24～72 h forecast validity time by 0.4 h, 34.3 km, and 21.1 km, respectively; compared with the ensemble mean, these errors are reduced by 1.1 h, 36.4 km, and 29.6 km, respectively. (3) Integrating the optimal members selected by the MCTES scheme with the control forecast members further reduces the typhoon track forecast errors. Compared with the ensemble mean, the average distance error in typhoon track for the control forecast was reduced by ﹣5.21 %～11.51 %; the MCTES scheme increased the reduction in average distance error to 13.42 %～19.83 %; and the integrated scheme of MCTES and control forecast further increased the reduction in average distance error to 16.28 %～20.83 %.