As the climate changes, it is imperative that we improve our understanding - and, ideally, prediction - of regional temperature variability. To do so, it is necessary to merge insights from observations, dynamical models, and statistical models. In this talk, I will take this combined approach to discuss questions about predictability and uncertainty. First, I will present work on predicting high-impact summer heat waves from climatic boundary conditions. Both the land and the sea surfaces can be used to provide skillful predictions of Eastern US heat waves up to seven weeks in advance. The results raise additional, open questions about inferring physical causality within the observations. Second, I will move from subseasonal to multidecadal timescales to illustrate the oft-large contribution of internal variability to observed or modeled regional temperature (and precipitation) trends. The importance of internal variability in multidecadal regional climate trends was first clearly demonstrated with initial condition ensembles; however, these ensembles can suffer from biases that limit their use for regional climate studies. As a complementary tool, I will present the Observational Large Ensemble (Obs-LE), a statistical model that can generate internal variability consistent with the observations at a regional scale. The Obs-LE can be used to quantify the contribution of internal variability to our uncertainty in the climate system's responses to forcing, and allows for new approaches to climate model evaluation.