Global warming impacts the hydrological cycle, affecting the seasonality and timing of extreme precipitation. Understanding historical changes in extreme precipitation occurrence is crucial for assessing their impacts. This study uses relative entropy to analyze historical changes in seasonality and timing of extreme daily precipitation occurrences on the global domain for 63 years of fifth generation of the European Reanalysis reanalysis data. Our analysis reveals distinct regional patterns of change. During the second half of the 20th century, Africa and Asia experienced high clustering of precipitation extremes. Over the past 60 years, clustering increased in Africa while becoming more spread out in Asia. North America and Australia had initially lower clustering and showed slight increases over time. Extreme events in extra-tropical land regions mainly occurred in summer, with modest shifts in timing. These findings have implications for risk assessments of natural hazard like flash floods and landslides, emphasizing the necessity for region-specific adaptation strategies.

Quantifying the magnitude and frequency of extreme precipitation events is key in translating climate observations to planning and engineering design. Past efforts have mostly focused on the estimation of daily extremes using gauge observations. Recent development of high-resolution global precipitation products, now allow estimation of global extremes. This research aims to quantitatively characterize the spatiotemporal behavior of precipitation extremes, by calculating extreme precipitation return levels for multiple durations on the global domain using the Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset. Both classical and novel extreme value distributions are used to provide insight into the spatial patterns of precipitation extremes. Our results show that the traditional Generalized Extreme Value (GEV) distribution and Peak-Over-Threshold (POT) methods, which only use the largest events to estimate precipitation extremes, are not spatially coherent. The recently developed Metastatistical Extreme Value (MEV) distribution, that includes all precipitation events, leads to smoother spatial patterns of local extremes. For durations of 5 and 10 days, however, there are less events per year to fit the distribution (37 and 22 on average, respectively), leading to larger inter-annual variability and possible overestimation of the extremes. While the GEV and POT methods predict a consistent shift from heavy to thin tails with increasing duration, the MEV method predicts a relatively constant heaviness of the tail for any precipitation duration, opening up an important research question on what is the ‘correct’ tail behavior of extreme precipitation for different durations. The generated extreme precipitation return levels and corresponding parameters are provided as the Global Precipitation EXtremes (GPEX) dataset. These data can be useful for studying the underlying physical processes causing the spatiotemporal variations of the heaviness of extreme precipitation distributions.