The computational complexity of distributed groundwater models poses a significant challenge in the conjunctive management of surface and groundwater resources. Here, we propose a multi-model framework to predict groundwater recharge using a simple water balance model. We compare and contrast two lumped parsimonious conceptual models of groundwater recharge, one based on P only and another, additionally including PE as a predictor. Both model variants also include an option to specify annually fixed or seasonally varying recharge factors, as well as a number of pumping scenarios. We incorporate these models within a python based open-source toolbox, REGSim (Recharge Estimation and Groundwater Simulation). The toolbox features a multi-objective evolutionary algorithm (NSGA-II), the Generalized Likelihood Uncertainty Estimation (GLUE) method, and regional sensitivity analysis (RSA). These functionalities allow the users to perform multi-objective calibration of the models, obtain confidence intervals on predicted groundwater heads, and understand the relative importance of different model parameters. REGSim is used to simulate groundwater heads for the urban agglomeration of Hyderabad, India. Using REGSim, we tested alternative conceptualizations of groundwater recharge and pumping processes in the city. We found that the intra-annual dynamics of groundwater levels are better explained by seasonally varying the recharge factors than annually fixed recharge factors. The model achieved an NSE of 0.64 and 0.70 during the validation for the formulations based on P only and using both P and PE, respectively. Furthermore, sensitivity analysis revealed that specific yield is the most influential parameter affecting the simulated groundwater head in the region. © 2021