The hydrological behavior and freshwater availability in any typical river basin are highly dependent upon precipitation making it the most crucial input variable for hydrological modelling. Precipitation as an input variable to hydrological models is available in gridded form with various spatiotemporal resolution. The variations in the model inputs could be subjected to uncertainties in the hydrological model simulation, which further affect the estimation of blue water flow (BWF) and green water flow (GWF) of a river basin. In this study, we investigated the effects of three gridded precipitation datasets [Watch forcing data ERA-Interim (WFDEI); Princeton datasets; Indian Meteorological Department (IMD)] on streamflow pattern, BWF, and GWF using a semi-distributed hydrological model [Soil and Water Assessment Tool (SWAT)] and a lumped rainfall-runoff model [Hydrological Simulation model (HYSIM)] in the Damodar river basin situated in eastern India. Both the models are simulated at daily time steps with the calibration of ten years (1994 – 2004) and validation of five years (2005 – 2010) at catchment outlet (Durgapur barrage) using three precipitation datasets. The performance of all the three precipitation products is evaluated on the basis of streamflow simulation for both HYSIM and SWAT model at the basin outlet using the performance indicators viz., Nash-Sutcliffe efficiency (NSE), coefficient of determination (R2) and percent bias (PBIAS). The seasonal and annual variation in precipitation values of the WFDEI, Princeton, and IMD dataset could attribute to the significant variations in the performance indicators. Subsequently, the best performance in streamflow simulation is achieved by HYSIM model compared to SWAT with IMD precipitation input. Both models showed remarkable differences in BWF and GWF estimation due to changes in precipitation inputs. The results also indicate that BWF is more sensitive to precipitation than GWF as BWF is directly generated from precipitation. All the above observations suggest that the choice of appropriate precipitation datasets is essential to examine the catchment hydrological behavior, and it further helps policymakers to make critical water management decisions.