Mesoscale convective systems (MCSs) are important phenomena in the global hydrologic cycle, and account for over 50% of precipitation received in some regions. Given this, it is important to understand how well they are represented by commonly used reanalysis products with long data availability such as ERA5. Herein MCS representation is studied using the PyFLEXTRKR algorithm, a flexible Python-based object tracking algorithm that uses brightness temperature and precipitation features to track MCSs over their lifespan. The algorithm is first tuned to ensure consistency of tracked global MCS statistics from our previous long term database (Feng et al. 2021) with coarsened versions of IMERG V6 and V7. Then, the algorithm is applied to these coarsened IMERG datasets from 2001 - 2020 for use as reference, as well as to ERA5 for the same time period, and the resulting precipitation statistics are compared. Our analysis will examine if ERA5 is able to detect global MCS precipitation statistics accurately, as geospatially compared to the reference data and with a quantitative focus on the fraction of precipitation that is MCS-related. The PyFLEXTRKR algorithm will then be applied to the ERA5 data from 1940 - 2023, creating a novel long-term record of MCSs on a global scale. Observed trends in MCS precipitation statistics over this time period will be further analyzed on regional scales to investigate the influence of climate change and internal variability on MCS behavior. The results of this work have implications for the study of MCSs over long time scales, particularly in regard to improving understanding of how precipitation patterns and severe weather hazards may change over time.