Abstract:One of the key constraints on the difficulty of short-range forecasting of warm-season precipitation in the Beijing-Tianjin-Hebei region is the lack of atmospheric dynamic parameter profiles and the unknown pre-precipitation signal. In this study, we analyzed the evolution of the dynamical field before the trigger of strong convection by using the dynamical parameter profiles inverted from the wind profile radar (RWP) mesoscale network in the warm season (April-September) of 2023-2024 and constructed the horizontal convergence intensity index before the trigger of strong convection. The results show that, before the trigger convection, the dynamical field is characterized by a vertical upward motion configuration with low-level convergence and high-level divergence. During the 30 min before precipitation, the continuously enhanced convergent and upward motion favors the occurrence of strong convection. Among 763 strong precipitation events, the horizontal convergence intensity index was able to effectively identify the strong precipitation events with an identification accuracy of 72.4%. This study develops a quantitative discrimination technique of strong convective precursor signals from RWP mesoscale network, which provides an important reference for quantitatively analyzing the early warning of strong convection in the Beijing-Tianjin-Hebei region.