In midnight on 31 July 2015, an extreme flashrain event was happened in Taihang Mountain. The rainfall was more than 100 mm at 5 automatic weather stations (AWSs) in mountain areas of Shijiazhuang and Xingtai, of which Yuantou town of Shijiazhuang City and Nanzhonggao Town of Xingtai City were caught by extreme shorttime intense rainfall with rainfall amount more than 50 mm·h-1 and more than 100 mm·(3 h)-1. However, the numerical prediction, the superior guidance forecast and meteorological observatory all failed to forecast this rainstorm. Based on conventional upperlevel and surface observations, AWS data, Doppler weather radar data and numerical forecast test, this paper analyzed the prediction ideas and the reason of failure. The event occurred under weak synopticscale background in northeast of Tibetan high. The key of forecasting severe precipitation was to grasp the effects of east wind in Taihang Mountain and terrain. The middlelevel northwest flow strengthened vertical wind shear, lowlevel shear line moved eastward, southwest wind strengthened warm moist air flows, and dry air layer overlaid on the thicken wet layer, strengthening the convective instability. The primary reason of the forecast error is that the lack of analyzing the maximum CAPE led to underestimating the unstable conditions and the development of thunderstorm at night in Shanxi Province. In addition, forecasters failed to use the unconventional data for realtime testing and correct numerical results, thus failing to predict the enhancement of thunderstorm enhance when it went down the hill. There is no effective conceptual model for severe precipitation conditions in the weak weather background at present. Forecast capacity of numerical model for such weather is poor. So forecasters need to improve the comprehensive analysis ability on observational data and the interpretation capacity of numerical prediction products in the future, and also to develop effective forecasting conceptual model through studying a large number of cases.