Due to their infrequency, the impacts of low-probability hydrologic conditions, such as extreme snowpack and drought, on watershed behavior can be difficult to assess in purely data-driven approaches. Large-scale process-based models can be used to simulate high-resolution data that capture hydrologic dynamics subject to low-probability events; however, large computational demands and complex model construction often limit the usability of these process-based simulations for stakeholders and decision makers. For that reason, this study presents a Machine Learning (ML) approach to learn streamflow dynamics from a large-scale process-based simulation in the Upper Colorado River Basin. Our goal is to create faster and more-interpretable tools that can still capture low frequency events. Long Short-Term Memory (LSTM) statistical emulators are used to predict daily, monthly, bi-annual, and long-term streamflow across a range of high- and low-frequency hydrologic conditions where observations may be too sparse to otherwise make accurate predictions. Here we explore first the ability of LSTM models to capture simulated streamflow from the process-based model and second the ability to transfer across the domain spatially. Our results will help bridge gaps between computationally intensive process-based simulations and purely data driven approaches.