Global climate models (GCMs) have been used extensively as a tool to investigate future changes in heavy precipitation. However, their spatial resolution is generally too coarse for their application for decision making and for impact studies at a more local scale. To mitigate these issues, dynamical downscaling of heavy precipitation using regional climate models (RCMs) can provide information at a spatial resolution that makes their outputs locally useful. Although many studies highlighted the improvements by RCMs in reproducing the precipitation processes, much less is known about their capability in reproducing the temporal clustering of heavy precipitation. Here we use Cox regression to investigate temporal clustering in heavy precipitation driven by four dominant large-scale climate modes (Arctic Oscillation, North Atlantic Oscillation, East-Atlantic Pattern, and Scandinavia Pattern) over Europe. Results are based on high-resolution RCMs (i.e., 0.11° and 0.44°) part of the Coordinated Downscaling Experiment-European Domain (EURO-CORDEX) and use observed daily precipitation from the E-OBS as reference data. In addition to analyses at the continental scale, we consider the added value of these simulations at a regional scale. We find that RCMs are skillful at capturing the observed temporal clustering of heavy precipitation events across Europe. Moreover, the 0.11°-RCMs did not perform better than their 0.44°-version; these findings can potentially suggest that the current configuration of these models has reached an upper limit of performance in reproducing the temporal clustering, and finer resolution and modeling may be necessary to increase the realism in reproducing the processes at play.