Lumped rainfall-runoff models are widely used for flow prediction, but a long-recognized need exists for diagnostic tools to determine whether the process-level behavior of a model aligns with the expectations inherent in its formulation. To this end, we develop a comprehensive exploration of dominant parameters in the Hymod, HBV, and Sacramento Soil Moisture Accounting (SAC-SMA) model structures. Model controls are isolated using time-varying Sobol′ sensitivity analysis for twelve MOPEX watersheds in the eastern United States over a 10 year period. Sensitivity indices are visualized along gradients of observed precipitation and streamflow to identify key behavioral differences between the three models and to connect these back to the models' underlying assumptions. Results indicate that the models' dominant parameters strongly depend on time-varying hydroclimatic conditions. Parameters associated with surface processes such as evapotranspiration and runoff generally dominate under dry conditions, when high evaporative fluxes are required for accurate simulation. Parameters associated with routing processes typically dominate under high-flow conditions, when performance depends on the timing of flow events. The results highlight significant inter-model differences in performance controls, even in cases where the models share similar process formulations. The dominant parameters identified can be counterintuitive; even these simple models represent complex, nonlinear systems, and the links between formulation and behavior are difficult to discern a priori as complexity increases. Scrutinizing the links between model formulation and behavior becomes an important diagnostic approach, particularly in applications such as predictions under change where dominant model controls will shift under hydrologic extremes.