![]() ![]() 1b, the distribution of wet-day intensity distributions shift towards higher values. As the wet-day frequency increases in Fig. The effects of orography on the wet-day frequency can also be seen over the Andes and Himalayas. Potential drivers for the observed relationships, such as large-scale and convective-precipitation processes, vertical motion and convective available potential energy are examined to determine the physical basis for these relationships.įigure 1a shows physically interpretable structures, such as regions of high wet-day frequency over the inter-tropical convergence zone and regions of low-precipitation frequency over desert regions. A range of extreme-precipitation metrics are also shown to be strongly positively correlated to the wet-day frequency. The statistical relationship between the wet-day frequency and mean wet-day intensity is shown to have a strong positive correlation in this framework. This analysis is repeated for satellite, gauge and other reanalysis datasets to determine the robustness of these relationships. Wet-day intensity distributions are then derived over these regions. In this Article, ECMWF Reanalysis v5 (ERA5) 10 output is examined to determine the frequency of precipitation, which is used to group regions by their wet-day frequency. The importance of the wet tail of the distribution is also highlighted in work that examined the geographic distribution of rainfall in different intensity classes and the frequency of each class 6, 9. For example, across Global Historical Climatology Network (GHCN) weather stations, half the annual precipitation falls during the wettest 12 days of the year 8. A small number of heavy-rainfall days contribute disproportionately to total precipitation compared with a large number of days of light precipitation 8. The exact distribution of wet-day intensity is important because the impact of extreme-precipitation events escalate drastically with increasing intensity 7. Wet-day intensity and wet-day-frequency metrics are also commonly used independently to evaluate the simulation of precipitation within numerical weather-prediction models, reanalyses and climate models 5, 6. Precipitation distributions and how they might change due to climate change are also critical for quantifying societal and economic risks 4 and are of particular importance in extreme events. The spatio-temporal distribution of precipitation has critical impacts on the availability of water resources 1, ecosystems 2, economic growth rates 3 and is also an important contributor to major hazards 4. Our results imply that wet-day frequency could be used to derive estimates of extreme-precipitation climate indices and corresponding uncertainties, these uncertainties being related to local processes. We also show that extreme-precipitation metrics are dependent on wet-day frequency within our framework. This suggests that the main drivers of precipitation are universal. These represent dynamic and thermodynamic processes that indicate how conducive wet-day-frequency regions are to large-scale and convective precipitation. We find the coherent relationship between wet-day frequency and intensity distributions is partially explained by wet-day-frequency regions having similar vertical velocity and convective available potential energy distributions, once polar regions are excluded. These wet-day-frequency regions are also more physically coherent than regions based on geographical location. ![]() ![]() When grouping global precipitation reanalysis data and observations from the past 40 years into regions of similar wet-day frequency, regardless of geographical separation, there is a strong correlation with wet-day intensity distributions. Here we show that where it rains more often, it also rains harder. Changes in precipitation climatology are typically based on independent analysis of precipitation frequency and intensity. Understanding precipitation is essential for quantifying weather and climate-related risks. ![]()
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