A Comparison of Moderate and Extreme ERA‐5 Daily Precipitation With Two Observational Data Sets

A comparison of moderate to extreme daily precipitation from the ERA-5 reanalysis by the European Centre for Medium-Range Weather Forecasts against two observational gridded data sets, EOBS and CMORPH, is presented. We assess the co-occurrence of precipitation days and compare the full precipitation distributions. The co-occurrence is quantified by the hit rate. An extended generalized Pareto distribution (EGPD) is fitted to the positive precipitation distribution at every grid point and confidence intervals of quantiles compared. The Kullback–Leibler divergence is used to quantify the distance between the entire EGPDs obtained from ERA-5 and the observations. For days exceeding the local 90th percentile, the mean hit rate is 65% between ERA-5 and EOBS (over Europe) and 60% between ERA-5 and CMORPH (globally). Generally, we find a decrease of the co-occurrence with increasing precipitation intensity. The agreement between ERA-5 and EOBS is weaker over the southern Mediterranean region and Iceland compared to the rest of Europe. Differences between ERA-5 and CMORPH are smallest over the oceans. Differences are largest over NW America, Central Asia, and land areas between 15°S and 15°N. The confidence intervals on quantiles are overlapping between ERA-5 and the observational data sets for more than 80% of the grid points on average. The intensity comparisons indicate an excellent agreement between ERA-5 and EOBS over Germany, Ireland, Sweden, and Finland, and a disagreement over areas where EOBS uses sparse input stations. ERA-5 and CMORPH precipitation intensity agree well over the midlatitudes and disagree over the tropics

Data challenges limit our global understanding of humanitarian disasters triggered by climate extremes

Compared to other natural disasters, climate-related extremes like droughts and heat waves have the largest impacts on human well-being at the global scale. Given that droughts are expected to intensify over the next decades, that other extreme events may happen more frequently, and that global population growth already now leads to a higher exposure and humanity faces major challenges. A fundamental question related to these critical global developments is whether increasing wealth levels could partly offset this phenomenon. In order to investigate this question systematically and at the global level we need to consolidate multiple data sets that are required in the respective global empirical analyses. In this contribution we review this challenge. We argue that an objective assessment of trends in global disaster risk due to climate extremes requires (1) accurate regional disaster impact data of extreme events, (2) local demographic and socioeconomic indicators, for example, on poverty, inequality, and education to understand the societal vulnerability, and (3) high-level information on the exact spatiotemporal dynamics of the climate extreme, its return-time probability, and its impacts on ecosystem services. We outline a path toward an integrated assessment of natural disasters that requires bridging the wide disciplinary gaps to understand the full cascade from climate extremes to human impact