Precipitation frequency analysis is important to the design of infrastructure. While analysis has traditionally been conducted using rain gauge data, quantitative precipitation estimates (QPEs) based on multi-sensor radar offers an opportunity for improvement. This study seeks to evaluate the implications of windfarm locations and weather radar coverage areas on radar rainfall frequency estimation. The analyses are based on 19 years of hourly Stage-IV radar data over the state of Iowa in the Midwestern United States. The data were compiled using an annual maximum series approach and a generalized extreme value (GEV) distribution to estimate pixel return quantiles. Results showed that windfarm locations positively correlate to elevated GEV shape parameters, resulting in a wider upper tail causing possible overestimation of extreme events. Probability of detection analysis revealed that areas roughly equidistant from multiple radars were more likely to record rainfall accumulations over all hourly thresholds tested. Radar based quantile estimates at windfarm locations and distances far from WSR-88D radar sites tended to be greater than gauge derived values while radar quantiles underestimated those based on observed values across the Iowa domain. This underestimation has been outlined as “conditional bias” by previous studies. While our analysis shows that these issues are overcome with sufficient expansion of reference windows, it strengthens the concerns of earlier studies suggesting radar-rainfall alone is not yet adequate for the determination of rainfall recurrence intervals used in engineering design.