A Multiscale Precipitation Forecasting Framework: Linking Teleconnections and Climate Dipoles to Seasonal and 24‐hr Extreme Rainfall Prediction

Abstract

AbstractWe 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.