A Multiscale Precipitation Forecasting Framework: Linking Teleconnections and Climate Dipoles to Seasonal and 24‐hr Extreme Rainfall Prediction. Issue 3 (3rd February 2020) - Kim, Yong‐Tak; So, Byung‐Jin; Kwon, Hyun‐Han; Lall, Upmanu
Abstract: We develop a hybrid statistical forecasting model for the simultaneous season‐ahead forecasting of both seasonal rainfall and the 24‐hr maximum rainfall for the upcoming season, using predictors identified through the Shared Reciprocal Nearest Neighbor approach. The model uses a generalized linear regression and a four‐parameter Beta distribution model for downscaling extremes using the predictors that were identified. A cross‐validation experiment for the last four decades in both Han‐River and Geum‐River watersheds, South Korea was performed to test the efficacy of the model. The leave‐one‐out cross‐validated seasonal precipitation forecast demonstrates a correlation that ranges from 0.69–0.78 to 0.68–0.76 for the Han‐River and Geum‐River watershed, respectively. Similarly, for the 24‐hr maximum rainfalls in the upcoming season, the cross‐validated correlations between the predicted and the observed values range from 0.67–0.73 to 0.50–0.63, for the two river basins. A discussion of the potential causes of the skill is offered. Key Points: A novel method developed for a simultaneous warm season‐ahead forecasting of both seasonal rainfall and the 24‐hr maximum rainfall The SST and SLP dipole‐like patterns identified through the SRRN are clearly related to summer precipitation over the Korean Peninsula The predictive skills in both seasonal rainfall and 24‐hr maximum rainfall appeared better than those of the persistence model Plain Language Summary: There have been many efforts to reliably predict summer rainfall anomalies over East Asia, including South Korea in the form of teleconnection. However, their potential predictability during the summer season has not been well demonstrated, in particular for prediction of intraseasonal extreme rainfall. The time‐lagged climate variables related to summer precipitation are derived and used as predictors in a predictive model for seasonal rainfall over the next three months. The predictive skills in both seasonal rainfall and 24‐hr maximum rainfall appeared comparable and comparatively better than those of the existing model. Especially, the obtained extreme prediction results were generally good agreement and favorable as compared with those of previous studies that have limited predictability for the intraseasonal extreme rainfall over South Korea.