Improved estimation of global soil moisture of different shared socioeconomic pathways for 2016-2099

We design a novel Transformer SM Simulation Net (TSMSNet) to conduct global monthly 0.5°×0.5° SM simulation of SSP1-2.6, SSP2-4.5, and SSP5-8.5 from 2016 to 2099. Nine qualified future SM datasets, along with corresponding spatial distribution dataset of error parameters, and geographic background data are selected as model inputs. The learning target is calculated through merging the merits from Soil Moisture Active Passive (SMAP) and European Centre for Medium-Range Weather Forecast Reanalysis v5-Land (ERA5-Land) SM. The results indicate the TSMSNet simulated SM (R = 0.68, ubRMSE = 0.045 m3/m3) performs notable superiority in matching both temporal variation and absolute value of in-situ measurements across different landcover and climate regions. Besides, the TSMSNet simulated SM could favorably match the long-term trend of learning target. TSMSNet simulated SM has an overwhelming drying trend during 2016 to 2099. The decline magnitude rises accompanied by SSP changing from sustainable pathway to fossil-fueled development. The areas with significant drying trend mainly distributed in plateau and mid-latitude region. In terms of land cover types, evident drying trends are found in cropland and forest. SM shows faster descent rate in livable areas than unlivable areas. In summary, we develop a reliable future SM dataset, that is expected to act as a valuable reference for understanding future water cycle patterns.