The remnants of Hurricane Ida caused major damage and loss of life in the Northeastern United States on September 1st, 2021. Over 40 people died in the storm within the United States, 11 of whom died in flooded basement apartments primarily built in low-lying areas and in some cases, upon filled-in historical water bodies within New York City. Damages to the state of New York were estimated to be 7.5-9 billion dollars, and within the City, 33,500 buildings were impacted and it is estimated that repairs to the subway system will cost 75-100 million. The storm was so catastrophic because the maximum hourly precipitation intensity, recorded as 3.47 inches at Central Park, was unprecedentedly high for the region- 75% higher than the previous record of 2 inches (Ida broke the hourly intensity record for the second time in 10 days, after Hurricane Henri). Given the complexity of precipitation scaling with global warming, it is important to understand how precipitation may change with climate change on a local scale, and thus we aim to contextualize this storm within the historical record. Using traditional planning metrics such as return levels and probabilities, we compare non-stationary techniques with stationary analyses to show the value of incorporating non-stationarity into extreme precipitation risk predictions. We show how the risk of a storm of Ida’s magnitude has varied over time and increased in certain periods with both stationary and non-stationary approaches, with the latter showing much higher probabilities overall decades before the occurrence of Ida. We predict the range of the risk of another similar storm (Ida-like event) in the future using two non-stationary models explaining the shift in the parameters of the extreme value distribution: using time as a variable, and using temperature projections from the GFDL-ESM4 model, SSP370 scenario. This non-stationarity analysis serves as a potential framework for city planners and climate scientists to view extreme precipitation risk through a new, dynamically shifting lens that will allow for better hazard mitigation strategies and preparation.