The hydrologic cycle is a complex and dynamic system of interacting processes. Hydrologists seeking to understand and predict these systems develop models of varying complexity, and compare their output to observations to evaluate their performance or diagnose shortcomings within the models. Often, these analyses take into account only single variables or isolated aspects of the hydrologic system. To explore how process interactions affect model performance we have developed a general framework based on information theory and conditional probabilities. We compare how conditional mutual information and mean square errors are related in a variety of hydrometeorological conditions. By exploring different regions of phase space we can quantify model strengths and weaknesses in terms of both process accuracy as well as classical performance. By considering a range of conditions we can evaluate and compare models outside of their average behavior. We apply this analysis to physically-based models (based on SUMMA), statistical models, and observations from FluxNet towers at a number of hydro-climatically diverse sites. By focusing on how the turbulent heat fluxes are affected by shortwave radiation, air temperature, and relative humidity we go beyond simple error metrics and are able to reason about model behavior in a physically motivated way. We find that the statistically based models, while showing better performance in the mean field, often do not represent the underlying physics as well as the physically based models. The statistically based model’s over-reliance on shortwave radiation inputs limits their ability to reproduce more complex phenomena.