Regional-scale intelligent optimization and topography impact in restoring global precipitation data gaps

Ground rainfall observations are essential for improving gauge-adjusted satellite precipitation estimates, particularly in the Global Precipitation Measurement mission era. However, over 60% of countries lack a complete surface-based rainfall gauge network, severely constraining the accuracy of global satellite precipitation retrievals. Here we present a model that integrates regional-scale intelligent optimization, topographic analysis, and an end-to-end neural network to merge multi-source precipitation data and reconstruct global spatiotemporal precipitation fields with resolved data gaps across scales. Our evaluation demonstrates that the model substantially improves satellite precipitation accuracy, increasing the correlation coefficient from 0.66 to 0.77. In particular, satellite estimates at high rain rates and over complex terrains show marked improvement when the model is applied. These findings suggest that the proposed approach holds strong potential for future applications in storm monitoring and flood forecasting, especially in mountainous regions with sparse observational data. Global hydrological research can be improved by a model that imputes and corrects global precipitation data gaps, according to an approach that integrates regional intelligent optimization, topographic analysis, and an end-to-end neural network to merge multi-source precipitation data.