Abstract: Objectives： Traditional hydrological models are difficult to accurately simulate hydrological processes, and the low spatial resolution of time-varying gravity field satellite data makes it difficult to accurately estimate changes in regional terrestrial water storage（ TWS） changes， a continuously operating GNSS reference station network can monitor in real-time the vertical deformation of the elastic crust caused by changes in surface hydrological loads. Methods： Combining the vertical displacement time series of GNSS terrestrial network reference stations with high precision and spatiotemporal resolution SSA-VMD（ Sparrow Search AlgorithmVariation Mode Decomposition) Nonlinear signal extraction algorithm estimates the spatiotemporal distribution characteristics of terrestrial water storage in Yunnan region from 2011 to 2020， Comparing and analyzing with time-varying gravity field data, global land data assimilation system, and precipitation data. Results:（1） Hydrological load is the primary factor causing seasonal variations in the time series of GNSS vertical displacement, with a correlation coefficient above 0.5. The second factor is non-tidal atmospheric load, and the least influential is non-tidal oceanic load. In response to the complex noise present in the time series of GNSS reference stations, the SSA-VMD method is employed to mitigate the impact of high-frequency noise, resulting in a cleaner processed time series.（2） The TWS variation in Yunnan Province exhibits a gradual increase from northeast to southwest in space. In a very small number of areas, due to the influence of other factors (such as evapotranspiration and runoff), there may be discrepancies with precipitation data. From the inversion results, the annual amplitude of TWS obtained from GNSS inversion is the largest, followed by GRACE/GFO, while the annual amplitude of TWS obtained from GLDAS is the smallest.（3） Compared with precipitation data, the TWS variations in Yunnan Province retrieved from GNSS, GRACE/GFO, and GLDAS exhibit a certain temporal lag. From 2011 to 2018, the TWS variations in Yunnan Province were relatively stable, showing an overall increasing trend. However, from 2019 to 2020, the EWH time series exhibited a significant downward trend, and the continuous decrease in water reserves across the province led to a prolonged severe drought. By August 2020, the water reserves across the province had basically returned to normal levels. Conclusions： The spatial and temporal distribution of land water storage obtained through GNSS inversion is generally consistent with GRACE/GFO and GLDAS data, showing a gradual decrease from southwest to northeast in Yunnan Province as a whole. However, the annual amplitude of land water storage changes obtained through GNSS inversion is larger than that of the other two methods, mainly because GNSS observation methods are more sensitive to changes in TWS in local areas. The seasonal variations in the GNSS-EWH, GRACE-EWH, and GLDAS-EWH time series are significant. When analyzed in conjunction with precipitation data, it is found that there is a certain lag between them.