Conventional observations, encryption automatic weather station, Doppler weather radar, windprofiling radar and NCEP reanalysis data are identified and examined to analyze the extensive severe convection falling area and lifting trigger mechanism in Shandong on 11 September 2016. The results show that under the influence of upper trough, unstable atmospheric stratification occurred over the regions whether or not convections, severe convective cloud cluster spread everywhere in the environments characterized by large convective available potential energy (CAPE) and little convective inhibition (CIN), thus the trigger of lifting become the key factor for severe convection’s occurrence. Lifting trigger mechanism was organized by surface convergence line, dry line, seabreeze front and gust flow. Because of the little convective inhibition, lifting force could be relatively weak causing various thunderstorm lifting mechanisms in different regions. Surface convergence line lifting caused the severe convection in Northwest Shandong, and the combination of seabreeze front and cold front caused the severe convection in Shandong Peninsula. Gust front of the preexisting thunderstorm cold pool boundary was the reason of severe convection in midland Shandong, while the interaction of dry line and surface convergence line caused the severe convection in Southeast Shandong. The magnitude of convergence line lifting force which can be measured by boundary’s divergence was a consequential element. Under the condition of the surface convergence line, mesoscale boundary of mass in different temperature and humidity became the determining factor of thunderstorm trigger. The omissive forecast of the southeast lowlevel flow in shortterm forecast which was a key mesoscale system caused the deviation of the forecast falling area. So, adjustment trend of model forecast in different initial times and seasons are the major reasons for the less intense forecast. Experience indicates that improving the correction ability of numerical model by analyzing a large number of cases is an effective method to raise the forecasting accuracy.