Statistical Bias Correction of Precipitation Forecasts Based on Quantile Mapping on the Sub-Seasonal to Seasonal Scale

Accurate precipitation forecasting is challenging, especially on the sub-seasonal to seasonal scale (14–90 days) which mandates bias correction. Quantile mapping (QM) has been employed as a universal method of correction it effective in correcting distribution attributes mean and variance, but neglects correlation between model observation data computing inefficiency large-scale applications. In this study, quantile matching threshold by time series (MPTT-QM) was proposed tackle these problems. The MPTT-QM applied correct FGOALS forecasts 14-day 90-day lead times for Pearl River Basin (PRB), taking IMERG-final product observation. justified comparing with original QM terms accumulation hydrological simulations. results show that not only improves spatial also effectively preserves temporal change, better detection ability. Moreover, MPTT-QM-corrected modeling performance runoff simulations than QM-corrected modeling, significantly increased KGE metrics ranging from 0.050 0.693. shows promising values improving utilities various forecasts.