We investigated the relationship between tropical cyclones (TCs) and the Pacific Decadal Oscillation (PDO) over the western North Pacific (WNP) during 1982–2018, focusing on an area surrounding the Korean Peninsula and Japan. The frequency of TCs passing through this area (TC_m) in the negative PDO phase was approximately 50% higher than that in the positive phase in September. Higher TC track density was observed around the Korean Peninsula and Japan in the negative PDO phase because the large‐scale environments were favorable for TC activity. However, TC_m and PDO were not related in August because the location of WNP subtropical high was not significantly different. In addition, large‐scale environments, which were associated with TC activity, provided more favorable conditions in September than in August.

Understanding how global warming affects tropical cyclone (TC) intensity and precipitation for target regions is essential to preparing for associated damages but detailed processes remain uncertain. This study provides the first quantification of anthropogenic influences on TC characteristics affecting South Korea using convection-permitting model (CPM) simulations (3 km resolution). For the observed four recent TCs that strongly affected South Korea, CPM simulations were performed under current (ALL) and counterfactual conditions without human influences (NAT). The observed sea surface temperature and lateral boundary conditions were used for ALL while changes attributable to human influences (estimated using CMIP6 multimodel simulations) were removed from observed boundary conditions for NAT runs. ALL experiments captured the observed TC intensity and precipitation reasonably. After removing human influences, TC intensity and precipitation were reduced in NAT experiments. Importantly, areas with extreme precipitation (i.e., having precipitation larger than 150 mm) were found to expand by 16–37% in ALL compared to NAT, which was induced by an enhanced upward motion near the TC core and an increase of background water vapor in line with warming. Further, the role of increased moisture was found to become important as TC moves to mid-latitudes. This study provides valuable insights into how greenhouse warming can intensify TC-induced extreme precipitation over East Asia.

Given their conditions to reside in and intensify longer over warm oceans, tropical cyclones (TCs) in the western North Pacific (WNP) present a stronger lifetime maximum intensity during El Niño than during La Niña. By using observational data, we found that the anomalously cool sea surface temperature (SST)s in the basin act as effective barriers against intense TCs approaching East Asia during El Niño, weakening their destructiveness at landfall. Based on our high-resolution pseudo-global-warming simulations, the basin-wide 2K SST warming within the WNP basin can, however, shatter this cool SST barrier, exposing East Asia to more destructive TCs during El Niño, compared to those during La Niña. Considering that the 2K warmer WNP will likely occur in the mid-21st century under a high emission scenario and in the late 21st century under a moderate emission scenario, our findings support that more aggressive efforts of global warming mitigation are needed.

Accurately simulating near-surface wind speeds is indispensable for wind energy development, particularly under extreme weather conditions. This study utilizes the Weather Research and Forecasting (WRF) model with a 6 km resolution to evaluate 80 m wind speed simulations over Europe, using the ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis version 5 (ERA5) as initial and lateral boundary conditions. Two cases were analyzed: a normal case with relatively weak winds, and an extreme case with intense cyclonic activity over 7 days, focusing on offshore wind farm regions and validated against Forschungsplattformen in Nord- und Ostsee (FINO) observational data. Sensitivity experiments were conducted by modifying key physical parameterizations associated with wind simulation to assess their impact on accuracy. Results reveal that while the model realistically captured temporal wind speed variations, errors were significantly amplified in extreme cases, with overestimation in weak wind regimes and underestimation in strong winds (approximately 1–3 m/s). The Asymmetrical Convective Model 2 (ACM2) planetary boundary layer (PBL) scheme demonstrated superior performance in extreme cases, while there were no significant differences among experiments under normal cases. These findings emphasize the critical role of physical parameterizations and the need for improved modeling approaches under extreme wind conditions. This research contributes to developing reliable wind speed simulations, supporting the operational stability of wind energy systems.

Accurate day-ahead wind speed forecasting is essential for wind power forecasting and electricity market participation under increasing renewable energy penetration. This study proposes a hybrid forecasting framework that combines raw global forecasts from GFS and IFS, the KMA KIM-RDAPS regional forecast, and dynamically downscaled GFS/IFS forecasts generated with alternative boundary-layer physics. Seven forecast members were synthesized using arithmetic averaging, performance-weighted averaging, and LightGBM-based machine learning (ML) regression. The framework was evaluated over Jeju Island, Republic of Korea, using 10 m Automatic Weather Station observations from 2023 to 2024 and 80 m meteorological mast observations from 2023. For the AWS evaluation, 2023 was used for training and validation, and 2024 was reserved for independent testing. The site-specific LightGBM synthesis achieved the most consistent improvement, reducing the median site-wise MAE across 31 AWS sites to 0.90 m s⁻¹, corresponding to a 39.2% improvement relative to the best non-downscaled member and 47.2% relative to the unweighted multi-model mean. In the 80 m mast-based diagnostic assessment, the same approach reduced derived normalized power MAE to 11.4%. These results indicate that ML synthesis of multi-source NWP forecasts can improve day-ahead wind speed and power-oriented forecast information over complex island terrain.