An extreme rainfall event over the United Arab Emirates in April 2024: Role of dynamic and thermodynamic drivers

On 16 April 2024, the United Arab Emirates (UAE) experienced an unprecedented Extreme Rainfall Event (ERE) that produced record-breaking accumulations and widespread flooding, particularly in Dubai. The highest 24-hr rainfall total was recorded at Khatm al-Shakla (Al Ain), with 254.8 mm. Several other stations also recorded exceptionally high totals exceeding 100 mm, including 233.3 mm at Kalba, 219.3 mm at Al Marmoom, and 144 mm at Dubai International Airport. This study investigated the dynamic and thermodynamic drivers of the event using the Multi Source Weighted Ensemble Precipitation (MSWEP) dataset and ERA5 reanalysis. The results indicate that, on 16 April, a deep mid-tropospheric trough and an associated cut off-low generated strong upper-level divergence and large-scale ascent over the UAE. At the same time, substantially enhanced moisture transport from the Arabian Sea (associated with to strong moisture advection) and the Red Sea (supported by the Red Sea Trough) increased precipitable water and intensified moisture convergence across the region. Positive sea surface temperature anomalies over the surrounding seas may have further supported lower-tropospheric moisture availability. A well-defined 700 hPa cyclonic circulation on 16 April further supported mesoscale convective organization, while moist static energy profiles indicated a highly energetic troposphere conducive to deep convection. In addition, the extremity of the event is consistent with springtime teleconnections associated with El Niño decay conditions, which may have contributed to a large-scale background environment favorable for above-normal April rainfall over the region, with composite anomalies of approximately 30%. These findings highlight the critical role of coupled dynamic and thermodynamic processes in generating high-impact rainfall extremes. Improving the understanding of these processes is essential for advancing predictive modeling capabilities and strengthening climate-resilient planning across the region, in alignment with the goals of the "Early Warning for All" initiative.