“Characteristic hydrologic behavior” or system “signatures” refer to system behaviors that are consistently observed, and they serve as excellent benchmarks or evaluation metrics for model behaviors. Here authors examined the connections between streamflow and water storage, and further used data mining to form hypotheses about what controls system behaviors. Inter-annual changes in low, median and high regimes of streamflow have important implications for flood control, irrigation, and ecologic and human health. The Gravity Recovery and Climate Experiment (GRACE) satellites record global terrestrial water storage anomalies (TWSA), providing an opportunity to observe, interpret, and potentially utilize the complex relationships between storage and full-flow-regime streamflow. Here we show that utilizable storage-streamflow correlations exist throughout vastly different climates in the continental US (CONUS) across low to high flow regimes. A panoramic framework, the storage-streamflow correlation spectrum (SSCS), is proposed to examine macroscopic gradients in these relationships. SSCS helps form, corroborate or reject hypotheses about basin hydrologic behaviors. SSCS patterns vary greatly over CONUS with climate, land surface and geologic conditions. Data mining analysis suggests that for catchments with hydrologic settings that favor storage over runoff, e.g., a large fraction of precipitation as snow, thick and highly permeable soil, SSCS values tend to be high. Based on our results, we form the hypotheses that groundwater flow dominates streamflows in Southeastern CONUS and Great Plains, while thin soils in a belt along the Appalachian Mountains impose a limit on water storage. SSCS also suggests shallow water table caused by high-bulk density soil and flat terrain induces rapid runoff in several regions. Our results highlight the importance of subsurface properties and groundwater flow in capturing flood and drought. We propose that SSCS can be used as a fundamental hydrologic signature to constrain models and to provide insights that lead us to better understand hydrologic functioning.