Providing a dynamic method to monitor water storage in the Yellow River Basin and obtaining spatial and temporal conclusions on real-time water storage changes is of great significance to the ecological protection and social development of the basin. In this study, we adopted GRACE/GRACE-FO satellite data and meteorological station data from 2003 to 2023 in the Yellow River basin, reconstructed the missing data through multi-layer perceptual neural network. We analysed the characteristics of the Tibetan Plateau region, the Loess Plateau region, and the downstream region by combining the differences in water resources recharge, and human activities under different climatic environments. We further united the basin as a whole, and obtained the sub-districts and basin overall temporal and spatial patterns of change of terrestrial water reserves and temporal patterns of change of climate elements, and then analysed the influencing factors. Finally, we compared and analysed data on climate and terrestrial water storage changes in the Loop and the Beijing-Tianjin-Hebei region to explore the effects of inter-basin water transfers (South-to-North Water Diversion Project) on the restoration of terrestrial water storage. The results showed as follows: 1)The overall land water reserves in the Yellow River Basin showed a downward trend, while the Qinghai-Tibet Plateau showed an upward trend, and the driving forces of regional water reserves were different. 2)There is a seasonal variation of land water reserves in different regions. There is a lag of 2~3 months between peak annual precipitation and peak annual water storage in the Loess Plateau region and downstream region, but not in the Tibetan Plateau region. 3)The cross-basin water transfer project can significantly restore regional land water reserves, which is an important reference for the management of the Yellow River Basin. In order to improve the accuracy of time-space scale estimation of terrestrial water storage changes, it is necessary to further focus on the fusion of multi-source geophysical data to explore the data downscaling method.