Recognizing that the frequency distribution of annual maximum floods at a given location may change over time in response to interannual and longer climate fluctuations, we compare two approaches for the estimation of flood quantiles conditional on selected climate indices that carry the signal of structured low-frequency climate variation, and influence the atmospheric mechanisms that modify local precipitation and flood potential. A parametric quantile regression approach and a semiparametric local likelihood approach are compared using synthetic data sets and for data from a streamflow gauging station in the western United States. Their relative utility in different settings for seasonal flood risk forecasting as well as for the assessment of long-term variation in flood potential is discussed.