Soil moisture is a key variable that contributes for flux partitioning into sensible and latent heat components. There is a pressing need to study the impacts of shallow groundwater table dynamics on antecedent soil moisture and corresponding feedbacks to atmosphere. In this study, we evaluate WRF‐NoahMP simulated soil moisture using two experimental configurations: one with the default, free drainage approach (CTL) and with Miguez‐Macho groundwater scheme (GW). Results are presented for the study conducted over Ganga River basin, India between the years 2008 and 2014 for all the three seasons (pre‐monsoon, monsoon, post‐monsoon). It was observed that the GW model runs, improve soil moisture in topmost and bottom‐most layers. We discovered that the simulations improved temporal negative bias of seasonal accumulation during monsoon by around 91 mm. In addition, the average negative bias in latent heat flux improved by around 28 W/m2. Compared to CTL, GW was found to contribute additional atmospheric moisture content ranging between 3% and 5%. Comparison with in‐situ ground water showed that model overestimates the ground water table depth. This can be attributed to the ability of the model to only simulate natural occurring variabilities and lack of inclusion of anthropogenic factors such as ground water pumping in the model. The groundwater trend in Ganga is declining at alarming rate. In addition, if over exploitation of groundwater is not monitored properly, water and food scarcity is an imminent threat to the country's economy. To the best of authors knowledge, this is the first study of its kind to be implemented over Ganga basin, which evaluates the role of shallow ground water table on regional climate using coupled atmosphere‐land surface‐groundwater model. In summary, we highlight that the groundwater component along with WRF‐NoahMP coupled model improves soil moisture and precipitation representation over the Ganga basin.