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Tropical cyclones present a serious threat to many coastal communities around the world. Many numerical weather prediction models provide deterministic forecasts with limited measures of their forecast uncertainty. Standard postprocessing techniques may struggle with extreme events or use a 30-day training window that will not adequately characterize the uncertainty of a tropical cyclone forecast. We propose a novel approach that leverages information from past storm events, using a hierarchical model to quantify uncertainty in the spatial correlation parameters of the forecast errors (modeled as Gaussian processes) for a numerical weather prediction model. This approach addresses a massive data problem by implementing a drastic dimension reduction through the assumption that the MLE and Hessian matrix represent all useful information from each tropical cyclone. From this, simulated forecast errors provide uncertainty quantification for future tropical cyclone forecasts. We apply this method to the North American Mesoscale model forecasts and use observations based on the Stage IV data product for 47 tropical cyclones between 2004 and 2017. For an incoming storm, our hierarchical framework combines the forecast from the North American Mesoscale model with the information from previous storms to create 95\% and 99\% prediction maps of rain. For six test storms from 2018 and 2019, these maps provide appropriate probabilistic coverage of observations. We show evidence from the log scoring rule that the proposed hierarchical framework performs best among competing methods.