Focusing on common and significant forecast errors-the zonal mean errors in the numerical prediction model, this report proposes an approach to improving the dynamical extended-range (monthly) prediction. Firstly, the monthly pentad-mean nonlinear dynamical regional prediction model of the zonal-mean height based on a large number of historical data is constituted by employing the reconstruction phase space theory and the spatio-temporal series predictive method. The zonal height thus produced is transformed to its counterpart in the numerical model and further used to revise the numerical model prediction during the integration process. In this way, the two different kinds of prediction are combined. The forecasting experimenal results show that the above hybrid approach not only reduces the systematical error of the numerical model, but also improves the forecast of the non-axisymmetric components due to the wave-flow interaction.