Abstract: Accurate precipitation forecasting is critical for managing flood risks in New York City (NYC). NYC’s approximately 72% impervious surface area often routes runoff directly to sewer systems with limited capacity (~ 44.45 mm/hr). NOAA’s High-Resolution Rapid Refresh (HRRR) model, a3-km grid spacing hourly-updating convection-allowing forecast system, provides quantitative precipitation forecasts (QPF) alongside other predicted variables for the continental United States. While the HRRR’s QPF performance has been evaluated over broad regions, assessments over small-scale urban coastal environments like NYC remain limited. Therefore, this research assesses HRRR performance in predicting where, when, and how much precipitation reaches NYC. This study evaluates HRRR QPF by comparing it with the gridded Analysis of Record for Calibration (AORC) dataset. Multi-year precipitation data are extracted, temporally and spatially aligned, and assessed via statistical and numerical analysis to evaluate HRRR’s accuracy in predicting timing, intensity, and spatial placement of rainfall. Additionally, the use of self-organizing maps is explored for the spatial verification of extreme events based on shared seasonal behavior, facilitating analysis despite their rarity and localized nature. The results expected from such methods will provide insight into potential systematic biases and spatial inaccuracies that may limit the HRRR’s performance for NYC, where limited drainage infrastructure and vulnerable populations heighten the need for more accurate precipitation forecasts. Understanding HRRR performance for urban hydrometeorology and its associated forecasting strengths and limitations will support improved flood preparedness, aid in future model developments, and drive enhancements in verification techniques for the HRRR and other numerical weather prediction models alike. The results are from the NOAA EPP/MSI CSC NERTO graduate internship project that was conducted with NOAA mentors, Dave Turner and Kelly Mahoney of Earth System Research Laboratories (ESRL), Oceanic and Atmospheric Research (OAR). The NERTO aligns with NOAA CESSRST’s goal to conduct NOAA mission-aligned collaborative research. The NERTO Assessment of High-Resolution Rapid Refresh (HRRR) Precipitation Forecasts for Urban Coastal Areas: New York City Testbed also deepened the intern’s understanding of NOAA’s operational forecasting systems, data assimilation techniques, and model verification processes, while enhancing competencies in statistical analysis, geospatial data integration, and the interpretation of high-resolution numerical weather prediction outputs for urban hydrometeorological applications.