Baseflow, or the groundwater component of streamflow, is an important source of water for several applications, from increasing demands on freshwater resources to ecosystem health. Despite its relevance, our understanding of the processes driving baseflow and its interannual variability is limited. In this study, we focus on 458 U.S. Geological Survey streamflow gauges that have at least 50 years of daily data. We use a statistical modeling framework to select a set of predictors that represent the role of climate (i.e., precipitation, temperature and antecedent wetness) and land use (harvested acres of corn and soybeans). The models are able to describe well the variability in monthly baseflow across the region, with an average correlation coefficient between the observational records and the median of the fitted distribution of 0.70 among all months. Our results indicate that precipitation and antecedent wetness are the strongest predictors, where the latter was selected the most often. Temperature is an important predictor during the spring when snow-related processes are the most relevant. Agriculture was frequently selected in the Cornbelt region during the growing season (from March to July). The results of this study can inform future watershed management that sustains low flows and improves water quality.