The combined effect of the Arctic Oscillation (AO) and Western Pacific (WP) teleconnection pattern on the temperature variation during the winter in the northern hemisphere and East Asia over the last 56 years (1958/1959–2013/2014) was investigated using NCEP/NCAR reanalysis data. The study results revealed that the effect of the AO on winter temperature in East Asia could be changed depending on the phase of the WP pattern in the North Pacific. The negative relationship between the temperature of East Asia (25–45°N, 110–145°E) and the AO increased when the AO and WP were in-phase with each other. Hence, when winter negative (positive) AO was accompanied by negative (positive) WP, negative (positive) temperature anomalies were dominant across the entire East Asia region. Conversely, when the AO and WP were out-of-phase, the winter temperature anomaly in East Asia did not show distinct changes. Furthermore, from the perspective of stationary planetary waves, the zonal wavenumber-2 patterns of sea level pressure and geopotential height at 500 hPa related to the East Asian winter monsoon (EAWM) circulation strengthened when the AO and WP were in-phase but were not significant for the out-of-phase condition. An index considering the effect of both AO and WP on East Asia winter temperature was proposed. The correlation between the index and the East Asia winter temperature was statistically significant at the 99 % confidence level. The index was correlated with synoptic characteristics of the EAWM, including the Siberian High, East Asian trough, East Asian jet stream and surface air temperature.

The potential dynamical predictability of the winter Arctic Oscillation (AO) is investigated using the ensemble hindcast from the Pusan National University coupled general circulation model (PNU-CGCM) over the 30-year period of 1981–2010. The analysis indicates that PNU-CGCM can not only reproduce the spatial distribution of the AO but also significantly simulate the AO's temporal variability. In addition, the coupled model performs well in terms of predicting the AO's impact on the Northern Hemisphere winter climate. These results reveal the coupled model's potential for dynamical forecasting of the climate over the mid-latitude to high latitude.

Improved daily precipitation estimations were attempted using the parameter-elevation regressions on a parameter-elevation regression on independent slopes model (PRISM) with inverse-distance weighting (IDW) and a precipitation-masking algorithm for precipitation areas. The PRISM (PRISM_ORG) suffers two overestimation problems when the daily precipitation is estimated: overestimation of the precipitation intensity in mountainous regions and overestimation of the local precipitation areas. In order to solve the problem of overestimating the precipitation intensity, we used the IDW technique that employs the same input stations as those used in the PRISM regression (PRISM_IDW). A precipitation-masking algorithm that selectively masks the precipitation estimation grid points was additionally applied to the PRISM_IDW results (PRISM_MSK). For 6 months from March to August 2012, daily precipitation data were produced in a horizontal resolution of 1 km based on the above two experiments and PRISM_ORG. Afterwards, each experiment was evaluated for improvements. The monthly root mean squared errors (RMSEs) of PRISM_IDW and PRISM_MSK were reduced by 0.83 mm/day and 0.86 mm/day, respectively, compared to PRISM_ORG.

This study compares the bias correction techniques of empirical quantile mapping (QM) and the Long Short-Term Memory (LSTM) machine learning model for summertime daily rainfall simulation focusing on precipitation-dependent bias and temporal variation. Numerical experiments using Weather Research and Forecasting (WRF) were conducted over South Korea with lateral boundary conditions of ERA5 reanalysis data. For the spatial distribution of mean summertime rainfall, the bias-uncorrected WRF simulation (WRF_RAW) showed dry bias for most of the region of South Korea. The WRF results corrected by QM and LSTM (WRF_QM and WRF_LSTM, respectively) were improved for the mean summer rainfall simulation with the root mean square error values of 0.17 and 0.69, respectively, which were smaller than those of the WRF_RAW (1.10). Although the WRF_QM performed better than the WRF_LSTM in terms of the summertime mean and monthly precipitation, the WRF_LSTM presented a closer interannual rainfall variation to the observation than the WRF_QM. The coefficient of determination for calendar-day mean rainfall was the highest in the following order: the WRF_LSTM (0.451), WRF_QM (0.230), and WRF_RAW (0.201). However, the WRF_LSTM had a limitation in reproducing extreme rainfall exceeding 50 mm/day due to the few cases of extreme precipitation in training data. Nevertheless, the WRF_LSTM better simulated the observed light-to-moderate precipitation (10–50 mm/day) than the others.

The 2019/20 winter was extremely warm globally and in the Northern Hemisphere extratropics. The main cause of climate extremes particularly in East Asia, was the extreme positive Arctic Oscillation (AO) event superimposed on steady global warming. The negligible trend in the AO over the preceding 41 years makes it possible to distinguish the roles of AO and global warming in the observed extremes. We estimate and compare contributions to January–March 2020 climate extremes by the AO and global warming represented by local temperature trends using the ERA5 reanalysis data. Based on results from a preliminary study, we estimate the contribution by global warming using linear regression while that by the AO using cubic regression, which is more restrained for the high AO index values than linear. The results show that the temperature extremes were mainly caused by the extreme positive AO event which accounts for approximately 3/4 of the observed temperature anomalies in northern East Asia and 2/3 in eastern East Asia. In southern East Asia, the AO contributes negligibly and positive temperature anomalies are related to global warming and local and regional impacts, particularly extreme sea surface temperature, enhance south-westerlies and local radiative forcing. General conclusion is that the observed strong positive temperature anomalies including extreme anomalies over East Asia could have been achieved only as a combined effect of the extreme positive AO event and global warming. Quantification of the roles of the AO and global warming in climate extremes helps to estimate future anomalies caused by extreme AO events as well as assess uncertainties in climate model projections.