The locally triggered or organizational development of mesoscale convective systems is the core and key for the accuracy of convective precipitation nowcasting. In this paper, combined with radar extrapolation prediction technology, expert system, and high-resolution numerical model system, a new adaptive convective precipitation ensemble nowcasting method combined with diagnosis from numerical weather prediction mode for Beijing area is developed Based on the precipitation extrapolation, APEN (adaptive precipitation ensemble nowcasting) uses convective diagnostic factors provided by the RMAPS-IN (Rapid-refresh Multi-scale Analysis and Prediction System — Integration) to calculate the probability of convection evolution (initiation, growth, and dissipation) by a fuzzy logic algorithm, and get an ensemble probability by disturbing diagnosis factor thresholds and weights. Then, based on the expert experience, the adjustment of convective precipitation based on the extrapolation of precipitation is carried out. APEN has been tested to make precipitation nowcasting with two cases (one is with heavy rainfall and the other is with weak rainfall) in Beijing. The results show that the ensemble convective evolution probability, which is based on the RMAPS-IN multi-diagnosis factors, can reflect the trend of the convective system in both strong and weak weather conditions. Based on the expert experience model, adjustments of convective precipitation under three states (convection initiation, growth, and dissipation) can reasonably represent the impact of precipitation by evolution of convective systems. Comparison of precipitation nowcasting by APEN and RMAPS-IN (an operational system of Beijing Meteorological Bureau) shows that, regardless of whether the convective system is stimulated by systemic convection activity or locally triggered, the precipitation area and precipitation intensity predicted by APEN are closer to observations. In particular, the precipitation intensity forecast after considering the effect of convective evolution is much better than RMAPS-IN. The experimental comparison demonstrates APEN’s advantages and application potential in convective precipitation forecasting.