This study assesses the regional climate projection newly generated within the framework of the national downscaling project in South Korea. To obtain fine-scale climate information (12.5 km), dynamical downscaling of the HadGEM2-AO global projections forced by the representative concentration pathway (RCP4.5 and RCP8.5) scenarios is performed using the Weather Research and Forecasting (WRF) modeling system. Changes in temperature and precipitation in terms of long-term trends, daily characteristics and extremes are presented by comparing two 30 yr periods (2041−2070 vs. 2071−2100) in which increasing rates of emission forcing between the RCP4.5 and RCP8.5 scenarios are relatively similar and quite different, respectively. The temperature increase presents a relevant trend, but the degree of warming varies in different periods and emission scenarios. While the temperature distribution from the RCP8.5 projection is continuously shifted toward warmer conditions by the end of the 21st century, the RCP4.5 projection appears to stabilize warming in accordance with emission forcing. This shift in distribution directly affects the magnitude of extremes, which enhances extreme hot days but reduces extreme cold days. Precipitation changes, however, do not respond monotonically to emission forcing, as they exhibit less sensitivity to different emission scenarios. An enhancement of high intensity precipitation and a reduction of weak intensity precipitation are discernible, implying an intensified hydrologic cycle. Changes in return levels of annual maximum precipitation suggest an increased probability of extreme precipitation with 20 yr and 50 yr return periods.

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For the comprehensive estimation of regional climate change over East Asia (EA) at the 2 and 3°C global warming levels (GWLs), the Köppen-Trewartha climate-type change is assessed with ensemble regional climate change projections in line with Coordinated Regional Climate Downscaling Experiment (CORDEX)-EA phase 2. Under the 2°C (3°C) GWL, 17.6% (25.2%) of the EA region is expected to undergo major climate-type changes. Tropical and subtropical climate types will expand northward, accompanied by increasing hydroclimatic intensity. Limiting GWL to 2°C shows benefits by preventing subtropical-type expansion around far EA. Desertification over inland regions of EA exhibits scenario dependency. Boreal and tundra climate types over high-latitude regions will tend to decrease rapidly, especially over the Tibetan Plateau. The results are expected to be a baseline assessment of climate change over EA under the 2 and 3°C GWLs above the preindustrial level.Plain Language Summary This paper investigates the Köppen-Trewartha climate-type changes over East Asia (EA) at the 2 and 3°C global warming levels (GWLs) with the ensemble regional climate simulations. Under the 2°C (3°C) GWL, 17.6% (25.2%) of the EA region is expected to undergo major climate-type changes. The result shows increased tropical, subtropical, and desert climate types accompanied by intensification of hydroclimatic stress. In addition, boreal and tundra climate types over high-latitude regions will tend to decrease rapidly, especially over the Tibetan Plateau under the 2 and 3°C GWLs.

A new model output statistics method -Ensemble Selective Simple Linear Regression (E-SSLR) -is developed based on SLR in order to increase the seasonal prediction skill of a Coupled General Circulation Model (CGCM) over the Maritime Continent (MC), a region with large model simulation errors. E-SSLR is applied to Pusan National University (PNU) CGCM hindcast over the MC region for the period of 1981-2010 to reduce the systematic model bias in boreal winter (DJF) seasonal mean precipitation and outgoing long-wave radiation (OLR) anomalies. Three oceanic indices (Nino 3.4, El Nino Modoki and Indian Ocean Dipole (IOD) Mode indices) and one atmospheric index (Southern Oscillation Index, SOI) produced from PNU CGCM hindcast are used as SLR predictor. E-SSLR consists of three steps: Selection, SLR and Ensemble. The selection and ensemble steps are added to the conventional SLR step to overcome the weakness of the linear regression method. In the selection step, the grids with a temporal correlation coefficient between predictor and predictand exceeding the threshold are selected. These grids (grid-selected) are corrected by SLR in the second step. For the grids that are grid-not-selected, the original CGCM results are used without further correction. This prevents insignificant statistical correction due to the application of low correlated predictors to the SLR. The correction effect of E-SSLR is analysed in terms of deterministic and categorical analyses. The result shows that the seasonal predictability of DJF seasonal precipitation and OLR in the MC region is increased by using E-SSLR, and this increment is statistically significant. The correction effect is larger when indices with high predictability that are closely correlated with the predictand are used as predictors.

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