Novel approaches to post-processing (calibrating) 2-m temperature forecasts are explored with the ensemble reforecast data set published by the NOAA Earth Systems Laboratory (Climate Analysis Branch). As in several previous studies, verification indicates that post-processing (calibrating) the ensemble may be necessary to provide meaningful probabilistic guidance to users. We apply a novel statistical correction approach by combining a selection of statistical correction approaches used in the literature [e.g. logistic regression, and quantile regression] under the general framework of quantile regression to improve forecasts at specific probability intervals. We explore enhancing local forecast skill by including regional information for the nearest grid forecast through the use of analogues conditioned on the regional weather environment and by using error time series. We also introduce climatological quantile probabilities in the calibration so that our approach ensures that the forecast PDF represented by the ensembles has skill no worse than either a forecast of persistence or climatology. Results for a few selected locations within different climatic regimes will be assessed using traditional (probabilistic) verification measures as well as a new measure we introduce to examine the utility of the ensemble spread as an estimator of forecast uncertainty.