Evaluating Bias-Correction Methods for Seasonal Dynamical Precipitation Forecasts

Abstract

Abstract Seasonal forecasting of climatological variables is important for water and climatic-related decision-making. Dynamical models provide seasonal forecasts up to one year in advance, but direct outputs from these models need to be bias-corrected prior to application by end users. Here, five bias-correction methods are applied to precipitation hindcasts from ECMWF’s fifth generation seasonal forecast system (SEAS5). We apply each method in two distinct ways; first to the ensemble mean and second to individual ensemble members, before deriving an ensemble mean. The performance of bias-correction methods in both schemes is assessed relative to the simple average of raw ensemble members as a benchmark. Results show that in general, bias correction of individual ensemble members before deriving an ensemble mean (scheme 2) is most skillful for more frequent precipitation values while bias correction of the ensemble mean (scheme 1) performed better for extreme high and low precipitation values. Irrespective of application scheme, all bias-correction methods improved precipitation hindcasts compared to the benchmark method for lead times up to 6 months, with the best performance obtained at one month lead time in winter.