Mesoscale convective system (MCS) is a complex of convective storms and these mesoscale storms grow to hundreds of kilometers in size and can last for more than a day. MCSs in the United States often lead to a significant amount of high-impact extreme events. However, to what extent MCSs are modulated by natural modes of climate variability remains an open question. Baroclinic Annular Mode (BAM) is a global scale natural mode of variability that dominates the extratropical regions on the subeasonal to seasonal time scale with a pronounced periodicity of 20-30 days. Our recent work further found that the periodic nature of BAM can translate to smaller scales in certain regions, making BAM potentially a new source of S2S predictability. This motivates us to investigate BAM’s potential in modulating serial clustering of extreme weather events especially MCSs.

In this work, we will investigate the connection between MCSs and BAM by identifying all the MCSs events in the US within each BAM event’s 20-30 day lifecycle. MCSs events are tracked based on the newly developed Global MCS dataset (Feng et al 2021). We will classify all the MCSs events into positive BAM phase and negative BAM phase, respectively, and calculate the conditional probability of the occurrence of MCSs during different BAM phases. By focusing on the modulation of MCSs by the large-scale variability in BAM, our work will have important implications for improving the forecast ability and climatological projection of MCSs in the United States.