EMMA-Tracker v1.0: A lifecycle-based algorithm for identifying and tracking mesoscale convective systems in observations and climate models

Abstract. Understanding the long-term climatology and physical drivers of Mesoscale Convective Systems (MCSs) in Europe is hindered by the lack of multi-decadal datasets and the difficulties distinguishing precipitation resulting from MCSs from synoptic scale frontal precipitation. Reference datasets for model evaluation that mix these physically distinct phenomena can cause misinterpreting climate model skill in representation of mesoscale convective processes. To address this, we introduce the EMMA-Tracker (Evolution-based MCS Model Assessment), a novel algorithm designed to identify and track MCSs using only standard model output variables. This intentional design choice enables a physically consistent comparison between observations and climate model ensembles, providing a pathway to investigate how MCS characteristics may evolve in a warming climate through the analysis of future projections. We apply this tracker to IMERG precipitation and ERA5-derived atmospheric instability to generate a 27-year (1998–2024) warm-season climatology— the longest reference dataset of European MCSs to date. The algorithm’s core innovation is a series of physics-based post-processing filters that utilize the system’s full spatiotemporal lifecycle to isolate coherently propagating MCSs from stationary thunderstorms and frontal rainbands. Our results show that these coherent MCSs are the dominant driver of extreme hourly precipitation. Their contribution systematically increases with hourly precipitation intensity, exceeding 60 % of heavy precipitation (P99.9) across most of continental Europe and 80 % over parts of the Mediterranean. The EMMA-Tracker provides both an observational reference for climatological studies and for targeted, process-oriented evaluation of regional and convection-permitting climate models.