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Rainfall ensemble forecasts have to be skillful for both low precipitationand extreme events. We present statistical post-processing methods based onQuantile Regression Forests (QRF) and Gradient Forests (GF) with a parametricextension for heavy-tailed distributions. Our goal is to improve ensemblequality for all types of precipitation events, heavy-tailed included, subjectto a good overall performance. Our hybrid proposed methods are applied to daily51-h forecasts of 6-h accumulated precipitation from 2012 to 2015 over Franceusing the M{\'e}t{\'e}o-France ensemble prediction system called PEARP. Theyprovide calibrated pre-dictive distributions and compete favourably withstate-of-the-art methods like Analogs method or Ensemble Model OutputStatistics. In particular, hybrid forest-based procedures appear to bring anadded value to the forecast of heavy rainfall.

Forest-based methods and ensemble model output statistics for rainfall ensemble forecasting