Statistical downscaling for daily precipitation in Korea using combined PRISM, RCM, and quantile mapping: Part 1, methodology and evaluation in historical simulation

摘要

In this study, we present the Parameter-elevation Relationships on Independent Slopes Model (PRISM)-based Dynamic downscaling Error correction (PRIDE) model, which is suitable for complex topographies, such as the Korean peninsula. The PRIDE model is constructed by combining the PRISM module, the Regional Climate Model (RCM) anomaly, and quantile mapping (QM) to produce high-resolution (1 km) grid data at a daily time scale. The results show that the systematic bias of the RCM was significantly reduced by simply substituting the climatological observational seasonal cycle at a daily timescale for each grid point obtained from the PRISM. QM was then applied to correct additional systematic bias by constructing the transfer functions under the cumulative density function framework between the model and observation using six types of transfer functions. K-fold cross-validation of the PRIDE model shows that the number of modeled precipitation days is approximately 90121% of the number of observed precipitation days for the five daily precipitation classes, indicating that the PRIDE model reasonably estimates the observational frequency of daily precipitation under a quantile framework. The relative Mean Absolute Error (MAE) is also discussed in the framework of the intensity of daily precipitation.