Prediction problems broadly deal with ascertaining the fate of fluctuations or instabilities through the dynamical system being modeled. Predictability is a measure of our ability to provide knowledge about events that have not yet transpired or phenomena that may be hitherto unobserved or unrecognized. The challenges associated with these two problems, that is, forecasting a future event and identifying a novel phenomenon, are distinctly different. Whereas the prediction of novel phenomena seeks to explore all possible logical space of a model's behavioral response, the prediction of future events seeks to constrain the model response to a specific trajectory of the known history to achieve the least uncertainty for the forecast. Predictability challenges have been categorized as initial value, boundary value, and parameter estimation problems. Here I discuss two additional types of challenges arising from the dynamic changes in the spatial complexity driven by evolving connectivity patterns during an event and cross-scale interactions in time and space. These latter two are critical elements in the context of human and climate-driven changes in the hydrologic cycle as they lead to structural change–induced new connectivity and cross-scale interaction patterns that have no historical precedence. To advance the science of prediction under environmental and human-induced changes, the critical issues lie in developing models that address these challenges and that are supported by suitable observational systems and diagnostic tools to enable adequate detection and attribution of model errors.