Summer heatwaves repeatedly affect extended regions in Europe, resulting in adverse economic, social, and ecological impacts. Recent events, e.g., the 2022 heatwave, also attract interest regarding the spatial shifts of their impact centers. Evaluations so far either investigated heatwave passage at pre-defined locations or employed algorithms to spatio-temporally track their core regions. Usually, the latter focus on single events, and thus often fail to generalize spatial heatwave tracks or ignore track characteristics. Here, we use a data-driven approach employing causal discovery to robustly characterize European heatwave tracks in single-model initial condition large ensemble (SMILE) climate simulations to overcome sampling uncertainties of observational records. This enables us to identify specific recurrent heatwave tracks, evaluate their preferential seasonal occurrence, and associate them with atmospheric pressure center shifts. Additionally, the evaluation of heatwave track representation in the SMILE extends standard model evaluation, which is mostly based on static statistics. We provide the first comprehensive analysis on heatwave tracks considering internal climate variability conducted within a SMILE, promoting the latter as a methodological testbed in climate extremes research.