Precipitation Bias Correction: A Novel Semi‐parametric Quantile Mapping Method

Abstract Bias correction methods are used to adjust simulations from global and regional climate models use them in informed decision‐making. Here we introduce a semi‐parametric quantile mapping (SPQM) method bias‐correct daily precipitation. This uses parametric probability distribution describe observations an empirical for simulations. Bias‐correction techniques typically the bias between observation historical correct projections. The SPQM however corrects based only on assuming detrended have same as observations. Thus, bias‐corrected preserve change signal, including changes magnitude dry, guarantee smooth transition future results compared with popular techniques, that is, delta (QDM) statistical transformation of CDF using splines (SSPLINE). performed well reproducing observed statistics, marginal distribution, wet dry spells. Comparatively, it at least equally QDM SSPLINE, specifically spells extreme quantiles. is further tested basin‐scale region. spatial variability statistics precipitation reproduced Overall, easy apply, yet robust bias‐correcting