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Extreme events significantly impact ecosystems and are predicted to increase in frequency and/or magnitude with climate change. Generalized extreme value (GEV) distributions describe most ecologically relevant extreme events, including hurricanes, wildfires, and disease spread. In climate science, the GEV is widely used as an accurate and flexible tool over large spatial scales (>10(5) km(2)) to study how changes in climate shift extreme events. However, ecologists rarely use the GEV to study how climate change affects populations. Here we show how to estimate a GEV for hurricanes at an ecologically relevant (<10(3) km(2)) spatial scale, and use the results in a stochastic, empirically based, matrix population model. As a case study, we use an understory shrub in southeast Florida, USA with hurricane-driven dynamics and measure the effects of change using the stochastic population growth rate. We use sensitivities to analyze how population growth rate is affected by changes in hurricane frequency and intensity, canopy damage levels, and canopy recovery rates. Our results emphasize the importance of accurately estimating location-specific storm frequency. In a rapidly changing world, our methods show how to combine realistic extreme event and population models to assess ecological impacts and to prioritize conservation actions for at-risk populations.