Abstract: Considering the highly non-linear characteristics of convective-scale weather systems and spatial-temporal uncertainties in high-resolution numerical forecasting, a convection-allowing ensemble forecast experiment has been conducted to simulate a strong squall line. The neighborhood probability (NP) method mainly considers the spatial displacement error in high resolution model forecasts, and cannot effectively address the problem of difference in event occurrence time between forecasts and observations. Therefore, a time factor is introduced into the NP method in this study. And based on the improved NP method and fractions skill score (FSS), the precipitation forecast of the strong squall line is verified on different spatial-temporal scales. The conclusions are as follows:(1) The FSS of extreme precipitation produced by the neighborhood ensemble probability (NEP) and probability matched mean (PMM) methods is higher than that produced by the traditional ensemble mean (EM) method, and the former two methods overcome the shortcomings of the EM method in predicting extreme precipitation. (2) For the squall line process investigated in the present study, the spatial scale of 15-45 km can reduce the spatial uncertainty in displacement error of precipitation forecast and optimize the forecast effect. The spatial scale of 15-30 km exhibits a better forecast capability for smaller-scale extreme precipitation events. (3) The convective-scale precipitation forecast has a corresponding relationship between temporal scale and rainfall intensity, and different temporal scales can capture temporal uncertainties in precipitation with different magnitudes. Meanwhile, the spatial and temporal scales are inter-related for the precipitation forecast effect. (4) The improved NP method can simultaneously show temporal and spatial uncertainties in high-resolution model forecast of convective-scale precipitation, achieve comprehensive evaluation of convective-scale precipitation on temporal and spatial scales, and provide skillful probabilistic forecast results for precipitation with various magnitudes and corresponding spatial-temporal scales.