As a fundamental component in water circulation, the spatio-temporal pattern of precipitation is critical for terrestrial hydrological cycle simulation. The satellite-based precipitation can describe the spatial pattern of precipitation properly, but the relatively low spatial resolution of the product limited its application in terrestrial hydrological cycle simulation. By taking Taihang Mountains as an example, based on the relationship between precipitation, altitude and NDVI (Normalized Difference Vegetation Index), the monthly GPM (Global Precipitation Measurement Mission) data from 2014 to 2016 are disaggregated to 1-km resolution with a GWRDL model (Geographically Weighted Regression Model coupled with Distributed Lagging). The results showed that with the aid of the altitude and NDVI, the GWRDL model could effectively downscale monthly GPM data. The spatial resolution of downscaled GPM data was increased by the GWRDL model, and the accuracy of the original GPM data was retained at the same time. Compared with precipitation downscaled by the Geographically Weighted Regression Model and Multiple Linear Regression Model, the precipitation downscaled by GWRDL model has highest coefficients of determination (R²), lowest root mean square error (RMSE) and lowest mean absolute error (MAE) with the observed data, indicating that using NDVI in other months as explanatory variable is better than that using NDVI in current month, and this practice improved the downscaling algorithm and highlighted the accuracy of downscaled precipitation. Because the relationship between precipitation and NDVI was closer in the next spring than that in winter, using NDVI in the following 2-3 months (NDVI in next spring) as an explanatory variable in GWRDL model can improve precipitation downscaling precision in winter. Although the GWRDL model, which gives a consideration of time lagging of NDVI, has a better performance than GWR model in winter, it is more suitable for precipitation downscaling in vegetation growing stage than in winter. Compared with original GPM data, the coefficients of determination between downscaled GPM data and observed precipitation was averagely increased by 0.02 with GWRDL model in vegetation growing stage (April to October), which is higher than that in winter (0.002). Therefore, we suggest that the GWRDL model should be used in GPM downscaling in vegetation growing seasons.