  Aiming to estimate extreme precipitation forecast quantiles, we propose a
nonparametric regression model that features a constant extreme value index.
Using local linear quantile regression and an extrapolation technique from
extreme value theory, we develop an estimator for conditional quantiles
corresponding to extreme high probability levels. We establish uniform
consistency and asymptotic normality of the estimators. In a simulation study,
we examine the performance of our estimator on finite samples in comparison
with a method assuming linear quantiles. On a precipitation data set in the
Netherlands, these estimators have greater predictive skill compared to the
upper member of ensemble forecasts provided by a numerical weather prediction
model.
