The potential of using a dynamical-statistical method for long-lead drought prediction was investigated. In particular, the APEC Climate Center (APCC) 1-Tier multi-model ensemble (MME) was downscaled for predicting the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) over 60 stations in South Korea. SPEI depends on both precipitation and temperature and can incorporate the impact of global warming on the balance between precipitation and evapotranspiration. It was determined that 1-Tier MME has difficulties in capturing the local temperature and rainfall variations over extratropical land areas and has no skill in predicting the two hydrological indices during boreal winter and spring. Conversely, temperature and precipitation predictions were substantially improved in the downscaled MME (DMME). In conjunction with variance inflation, DMME can give reasonably skillful six-month-lead forecasts of SPI and SPEI for the winter-to-spring period. Our results could lead to more reliable hydrological extreme predictions for policymakers and stakeholders in the water management sector in addition to better disaster mitigation and climate adaptations.

1. INTRODUCTION

2. DATASETS AND PREDICTION PROCEDURE

2.1 Datasets

2.2 Statistical downscaling

2.3 SPI and SPEI

2.4 Climatic water balance and PET

3. SIX-MONTH-LEAD DMME PREDICTION FOR HYDROLOGICAL EXTREMES IN SOUTH KOREA

3.1 Model simulated hydrological extremes

3.2 Long-lead station-scale climate prediction from downscaling

3.3 Prediction of extreme hydrological drought and flood episodes

4. CONCLUDING REMARK