Mixed-frequency extreme value regression: estimating the effect of mesoscale convective systems on extreme rainfall intensity

Understanding and modeling the determinants of extreme hourly rainfall intensity is of utmost importance for the management of flash-flood risk. In-creasing evidence shows that mesoscale convective systems (MCS) are the principal driver of extreme rainfall intensity in the United States. We use ex-treme value statistics to investigate the relationship between MCS activity and extreme hourly rainfall intensity in Greater St. Louis, an area particularly vul-nerable to flash floods. Using a block maxima approach with monthly blocks, we find that the impact of MCS activity on monthly maxima is not homo-geneous within the month/block. To appropriately capture this relationship, we develop a mixed-frequency extreme value regression framework accom-modating a covariate sampled at a frequency higher than that of the extreme observation.