We investigate the problem of threshold selection in the context of the extreme value regression model pioneered by Davison and Smith (1990), and finding its use in the analysis of factors affecting the likelihood of extremes events. In this regression context, the threshold choice is a non-trivial task and can have important consequences on the final estimates, since it should also depend on the covariates. We propose an efficient solution to automatically estimate these thresholds with the help of conditional splicing distributions, in the idea of the distributional regression machinery (Rigby and Stasinopoulos, 2005). We introduce a weighted likelihood estimator robust to a misspecification of the density of the body of the distribution that additionally accounts for uncertainty stemming from the threshold choice and respect the threshold stability property. The method is latter used in two applications: the estimation of the downside risks of around 10,000 hedge funds characterized by short available historical data, and the characterization of extreme climate events at different weather stations.