Society would greatly benefit from reliable early warnings of weather-related risk. A good physical understanding is needed construct reliable statistical forecast models. In this project we aim to understand the dynamical mechanisms that lead to (persistent) summer extremes. By combining causal inference techniques, machine learning and expert knowledge, we try to improve both physical understanding