Intensity, duration, frequency (IDF) curves are important decision-relevant metrics for stakeholders. However, uncertainty around the IDF estimates is one of the major concerns for stakeholders. The paper proposes a novel extension of current methods through pooling model data to reduce uncertainty around the multimodel IDF estimates. The method has three steps: (1) evaluate the performance of climate models in simulating the spatial and temporal variability of the observed annual maximum precipitation (AMP), (ii) bias-correct and pool historical and future AMP data of reasonably performing models, and (iii) compute IDF estimates in a nonstationary framework from pooled historical and future model data. Through Monte Carlo simulations with synthetic data, we show that return periods derived from pooled data have smaller biases and lesser uncertainty than those derived from ensembles of individual model data. When applied to the NA-CORDEX models, our method identifies significant future changes at more stations compared to median-based IDF estimates. The analysis suggests that almost all stations over the Susquehanna and at least two-thirds of the stations over the Florida peninsula will observe significant increases in 24-h precipitation for 2–100-yr return periods.