Bridging the scale gap: enhancing point-scale rainfall estimates by post-processing ERA5

Accurately estimating rainfall distributions, from-small-to-extreme totals, is crucial for addressing various environmental challenges (e.g., flood forecasting, water resource management, disaster preparedness). Global Numerical Weather Prediction (NWP) models can provide useful rainfall estimates; yet, they often misrepresent point-scale observations from rain gauges, underestimating the frequency of small rainfall totals and extreme values. In general, finer resolutions yield more accurate representation of gauge-based climatologies. Hence, this study provides a systematic, global verification of four NWP-modelled rainfall datasets of differing resolutions (with “resolution” meaning “horizontal grid spacing”) - ERA5’s Ensemble Data Assimilation (62 km, probabilistic), ERA5’s short-range forecasts (31 km, deterministic), short-range 46r1 ECMWF reforecasts (18 km, control run), and ERA5-ecPoint (point-scale, probabilistic)—against 20 years of global rain gauge observations, assessing each dataset’s ability to represent the entire rainfall distribution. Although in very mountainous areas (e.g., the Andes) ERA5-ecPoint underestimates zero-rainfall frequency and overestimates wet tail length, it dramatically improves upon raw NWP performance in many other regions by capturing more accurately the frequency of zeros, the “growth rates” of rainfall totals, and the wet tails. Moreover, due to its probabilistic nature, ERA5-ecPoint can estimate long return periods (e.g., 1000 years) without using distribution fitting, thereby offering valuable insights into extremely rare or unprecedented events at specific locations. Such findings underscore the importance of using post-processing to enhance the local-scale validity of global NWP models. Moreover, as climate change intensifies extreme rainfall events, such post-processing becomes crucial for estimating accurate long-period rainfall climatologies, as needed for effective mitigation and resilience building, particularly in areas lacking comprehensive and reliable rain gauge records.