Author(s): Kangmin Lee; Seogyun Lee; Yunsung Kim; Hyun-Han Kwon
Linked Author(s): Hyun-Han Kwon
Keywords: Pmp rcms ecis mccv gic
Abstract: This study employed five regional climate models (RCMs) from the Korea Meteorological Administration (HadGEM3-RA, RegCM4, CCLM, GRIMs, WRF) to evaluate extreme climate events using 25 Extreme Climate Indices (ECIs), consisting of 7 precipitation-based and 18 temperature-based indices. Percentile-based thresholds were used to assess the accuracy of extreme event representation by each model, while inter-model independence was verified through correlation analysis of ECIs. Methodologies such as Monte Carlo Cross-Validation (MCCV), Generalized Information Criteria (GIC), and correlation-based distance metrics were applied to identify the most suitable climate change scenarios. Among the models analyzed, WRF and GRIMS were identified as the most independent and reliable models. Future research should focus on exploring ECIs tailored to specific design values, including design floods, design rainfalls, drought analysis, and PMP. More importantly, this targeted methodology builds upon the general model selection framework developed in this study, aiming to enhance scenario-specific evaluations for hydrological and climate adaptation applications.
Year: 2025