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.

In this study, we examined the potential of the Korea Rapid-Development Thunderstorm (K-RDT) product obtained from a geostationary meteorological satellite to improve the short-term prediction of heavy rainfall caused by a mesoscale convective system over South Korea. Specifically, we utilized a simple nudging technique to integrate K-RDT data into the Simplified Arakawa Schubert (SAS) deep convection scheme of the Global/Regional Integrated Model System (GRIMs) Regional Model program (RMP). Our analysis focuses on selected cases of heavy rainfall. The nudging experiments outperformed the control experiments in terms of precipitation forecasts. Notably, the experiment that used longer nudging times produced the best results. Our results also demonstrate that the K-RDT, with its resolution of 1 km, can detect small-scale convective cells that have clear impacts on large-scale atmospheric fields. This suggests that incorporating such small-scale information into numerical weather prediction (NWP) models can significantly improve forecasting skill, especially when the model cannot represent subgrid-scale convection.

This study investigates the impact of the scale-aware convective parameterization scheme (CPS) on convective cells related to simulation of heavy precipitation across the gray-zone using the Weather Research and Forecasting (WRF) model. We select the Kain-Fritsch (KF) and Multi-scale Kain-Fritsch (MSKF) schemes as non-scale-aware and scale-aware CPSs, respectively. The MSKF scheme uses a scale-aware parameter that modulates the convective available potential energy (CAPE) timescale and entrainment process in the KF scheme as a function of the horizontal grid spacing. This study shows that simulation of convection only with grid-scale process microphysics parameterization scheme (MPS) (i.e., explicitly resolved) causes an unreasonably overestimated and erroneous location of precipitation in the gray-zone because convection and atmospheric instability could not properly be triggered and reduced. Contrarily, the CPS without scale-awareness in the gray-zone exaggerates the convection and distorts synoptic fieldsleading to the erroneous simulation of heavy precipitation at high resolution. Contrastingly, the MSKF scheme with scale-awareness improves simulated convective cells related to heavy rainfall by removing atmospheric instability in the gray-zone, smoothly reducing the role of CPS and increasing the role of MPS as grid spacing is decreased. Additionally, the sensitivity experiments show that the shorter CAPE timescale and decreased entrainment process resulted in fast development and exaggeration of convective activities, respectively. These parameters modulated by the scale-aware MSKF scheme can play a crucial role in the balanced effect between the CPS and MPS in the gray-zone by controlling the entrainment rate and CAPE timescale.