Subsurface stormflow is an important component of total runoff generation processes, which influence soil moisture and evapotranspiration that interact with the atmosphere. However, its contribution is poorly represented in the current generation of land surface models. Due to the lack of physical basis of common parameterizations, significant efforts are required to calibrate parameters in global land surface models and to collect data needed for the calibration. A team of scientists from University of Illinois and the Department of Energy’s Pacific Northwest National Laboratory explored the methods to parameterize the subsurface flow generation process using both data analysis and model simulation. Focusing on empirical data analysis, this study assembled the controlling climate, soil and topographic characteristics as well as streamflow data for 50 eastern United States catchments. Detailed regression analyses were performed between two parameters of the subsurface generation process and the assembled catchment characteristics. Results indicated that one of the coefficients in the regressed relationship is closely related to the catchment hydrologic characteristics, while the other one is more strongly affected by climate (aridity) at the catchment scale. These results point to the need for more detailed exploration of the co-dependence of soil, vegetation and topography with climate in controlling subsurface stormflow.