Reliable precipitation data are essential for fine-scale hydrological applications at the regional level. Consequently, numerous studies have sought to generate high-resolution and high-accuracy precipitation products through spatial downscaling of satellite-based precipitation estimates and bias correction using ground observations. However, few such studies have considered the sub-daily scale, which holds greater application value. In this study, a cross-temporal "downscaling-fusion" framework, termed CTDF, is proposed. Both stages employ extreme gradient boosting (XGBoost) modeling: the first stage spatially downscales daily GPM precipitation from 0.1° to 0.01° using various high-resolution environmental factors, while the second stage fuses the downscaled GPM, cloud properties, and rain gauge observations to generate the final hourly precipitation estimates. With Youxian District, China as the study area, the performance of CTDF was compared against five alternative schemes, and the spatial distribution of the generated precipitation was analyzed. Results indicate that: (1) CTDF exhibits the best overall performance (CC = 0.81, MAE = 0.88 mm, RMSE = 1.95 mm, Bias = 0.4 %), mitigating the systematic underestimation inherent in the original GPM product, while omitting either stage results in performance degradation; (2) CTDF demonstrates more robust performance across different precipitation intensities and diurnal conditions; (3) CTDF substantially enhances the representation of spatial precipitation heterogeneity, increasing the coefficient of variation (CV) of GPM by 170 % and 255 % for convective and stratiform precipitation events, respectively. Overall, the two-stage collaborative design of CTDF achieves spatial refinement and accuracy improvement, providing a viable technical pathway for generating high spatiotemporal resolution precipitation products.