Studies have shown that regional climate models (RCMs) can simulate local climates at a higher resolution for specific regions compared to global climate models (GCMs), making dynamic downscaling using RCMs a more effective approach. Therefore, RCMs have become valuable tools for evaluating the potential impacts of climate change on specific regions and for informing local adaptation strategies. To fully understand the added value (AV) of RCMs, it is essential to understand how the characteristics differ between land and ocean. The complex topography of East Asia, including land and sea, makes it a suitable region for evaluating the AV of RCMs. In this study, we compared two regional simulations that integrated the same RCMs but employed different GCMs from the Coordinated Regional Climate Downscaling Experiment for their ability to simulate storm tracks in East Asia. The results of the RCMs over a historical period were compared with their host Coupled Model Intercomparison Project GCM projections and high-resolution reanalysis. In mountainous regions, the AV of the RCMs weakened the bias of the GCM and improved its agreement with the reanalysis. In plains and coastal areas, owing to the increase in horizontal resolution in RCMs, small-scale phenomena are well represented, and the storm track of RCMs shows similar values to that of the GCM in maritime regions. This study demonstrates the value of RCMs for improving the accuracy of climate projections in East Asia, informing adaptation strategies, and enhancing climate research.

Projected changes in extreme climate are occasionally predicted through multi-model ensemble methods using a weighted averaging that combines predictions from individual simulation models. To predict future changes in precipitation extremes, observed data and 21 of the Coupled Model Inter-comparison Project Phase 6 (CMIP6) models are examined for 46 grids over the Korean peninsula. We apply the generalized extreme value distribution (GEVD) to the series of annual maximum daily precipitation (AMP1) data. Simulation data under three shared socioeconomic pathway (SSP) scenarios, namely, SSP2-4.5, SSP3-7.0, and SSP5-8.5, are used. A multivariate bias correction technique that considers the spatial dependency between nearby grids is applied to these simulation data. In addition, a model weighting approach that accounts for both performance and independence (PI-weighting) is employed. In this study, we estimate the future changes in precipitation extremes in the Korean peninsula using the multiple CMIP6 models and PI-weighting method. In applying the PI-weighting, we suggest simple ways for selecting two shape 1 parameters based on the chi-square statistic and entropy. Variance decomposition with the interaction term between the CMIP6 model and the SSP scenario is applied to quantify the uncertainty of projecting the future AMP1. Return levels spanning over 20 and 50 years, as well as the return periods relative to the reference years (1973-2014), are estimated for three future overlapping periods, namely, period 1 (2021-2050), period 2 (2046-2075), and period 3 (2071-2100). From these analyses, we estimate that relative increases in the observations for the spatial median 20-year (50-year) return level will be approximately 16.4% (16.5%) in the SSP2-4.5, 22.9% (22.8%) in the SSP3-7.0, and 37.6% (35.4%) in the SSP5-8.5 scenarios, respectively, by the end of the 21st century. The expected frequency of the reoccurring years, particularly for the AMP1 from 150 mm to 300 mm under the SSP5-8.5 scenario, are projected to increase by approximately 1.4 times that of the past 30 years for period 1, approximately 2.3 times that for period 2, and approximately 3.5 times that for period 3. From the analysis based on latitude, severe rainfall was found to be more prominent in the southern and central parts of the Korean peninsula.