Surface soil moisture is projected to decrease under global warming. Such projections are mostly based on climate models, which show large uncertainty (i.e., inter-model spread) partly due to inadequate observational constraint. Here we identify strong physically-based emergent relationships between soil moisture change (2070–2099 minus 1980–2014) and recent air temperature and precipitation trends across an ensemble of climate models. We extend the commonly used univariate Emergent Constraints to a bivariate method and use observed temperature and precipitation trends to constrain global soil moisture changes. Our results show that the bivariate emergent constraints can reduce soil moisture change uncertainty by 7.87%, which is four times more effective than traditional temperature-based univariate constraints. The bivariate emergent constraints change the sign of soil moisture change from negative to positive for semi-arid, dry sub-humid and humid regions and global land as a whole, but exacerbates the drying trend in arid and hyper-arid regions.