Bias correction of daily precipitation over South Korea from the Long-Term Reanalysis using a Composite Gamma-Pareto Distribution Approach

Long-term precipitation data plays an important role in climate impact studies, but the observation for a given catchment is very limited. To significantly expand our sample size for the extreme rainfall analysis, we considered ERA-20c, a century-long reanalysis daily precipitation provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Preliminary studies have already indicated that ERA-20c can reproduce the mean reasonably well, but rainfall intensity is underestimated while wet-day frequency is overestimated. Thus, we first adopted a relatively simple approach to adjust the frequency of wet-days by imposing an optimal threshold. Moreover, we introduced a quantile mapping approach based on a composite distribution of a generalized Pareto distribution for the upper tail (e.g. 95th and 99th percentile), and a gamma distribution for the interior part of the distribution. The proposed composite distributions provide a significant reduction of the biases over the conventional method for the extremes. We suggested an interpolation method for the set of parameters of bias correction approach in ungauged catchments. A comparison of the corrected precipitation using spatially interpolated parameters shows that the proposed modelling scheme, particularly with the 99th percentile, can reliably reduce the systematic bias.