Mesoscale convective systems (MCSs) are clusters of thunderstorms that are important in Earth’s water and energy cycle. Additionally, they are responsible for extreme events such as large hail, strong winds, and extreme precipitation. Automated object-based analyses that track MCSs have become popular since they allow us to identify and follow MCSs over their entire life cycle in a Lagrangian framework. This rise in popularity was accompanied by an increasing number of MCS tracking algorithms, however, little is known about how sensitive analyses are concerning the MCS tracker formulation. Here, we assess differences between six MCS tracking algorithms on South American MCS characteristics and evaluating MCSs in kilometer-scale simulations with observational-based MCSs over three years. All trackers are run with a common set of MCS classification criteria to isolate tracker formulation differences. The tracker formulation substantially impacts MCS characteristics such as frequency, size, duration, and contribution to total precipitation. The evaluation of simulated MCS characteristics is less sensitive to the tracker formulation and all trackers agree that the model can capture MCS characteristics well across different South American climate zones. Dominant sources of uncertainty are the segmentation of cloud systems and the treatment of splitting and merging of storms in MCS trackers. Our results highlight that comparing MCS analyses that use different tracking algorithms is challenging. We provide general guidelines on how MCS characteristics compare between trackers to facilitate a more robust assessment of MCS statistics in future studies.

Mesoscale convective systems (MCSs) have been identified as an important source of precipitation in the Tibetan Plateau (TP) region. However, the characteristics and structure of MCS-induced precipitation are not well understood. Infrared satellite imagery has been used for MCS tracking, but cirrus clouds or cold surfaces can cause misclassifications of MCS in mountain regions. We therefore combine brightness temperatures from IR imagery with satellite precipitation data from GPM and track MCSs over the TP, at the boundary of the TP (TPB) and in the surrounding lower-elevation plains (LE) between 2000 and 2019. We show that MCSs are less frequent over the TP than earlier studies have suggested and most MCSs over land occur over the Indo-Gangetic Plain (LE) and the south of the Himalayas (TPB). In the LE and TPB, MCSs have produced 10 % to 55 % of the total summer precipitation (10 % to 70 % of summer extreme precipitation), whereas MCSs over the TP account for only 1 % to 10 \% to the total summer precipitation (1 % to 30 % of the total summer extreme precipitation). Our results also show that MCSs that produce the largest amounts of convective precipitation are characterized by longevity and large extents rather than by high intensities. These are mainly located south of the TP, whereas smaller-scale convection makes a greater contribution to total and total extreme precipitation over the TP. These results highlight the importance of convective scale modeling to improve our understanding of precipitation dynamics over the TP.