Because remote sensing (RS) data are spatially and temporally explicit and available across the globe, they have the potential to be used for predicting runoff in ungauged or poorly gauged catchments, a challenging area of research in hydrology over the last several decades. There is potential to use remotely sensed data for calibrating hydrological models in regions with limited streamflow gauges. This study conducts a comprehensive investigation on how to incorporate gridded remotely sensed-evapotranspiration (AET) and water storage data for constraining hydrological model calibration in order to predict daily and monthly runoff in 30 catchments of Yalong River basin, China. To this end, seven RS data calibration schemes are explored, compared to traditional calibration against observed runoff and traditional regionalization using spatial proximity. Our results show that using bias-corrected remotely sensed AET (bias-corrected PML-AET data) for constraining model calibration performs much better than using the non bias-corrected remotely sensed AET data (non bias-corrected AET obtained from PML model estimate). Using the bias-corrected PML-AET data in a gridded way is much better than that in a lumped way, and outperforms the traditional regionalization approach especially at upstream and large catchments. Combining the bias-corrected PML-AET and GRACE water storage data performs similarly to using the bias-corrected PML-AET data only. This study demonstrates that and there is great potential to use RS-AET based data for calibrating hydrological models in order to predict runoff in data sparse regions with complex terrain conditions.