Kyung-Ae Park1,2 and Eun-Young Lee1 1 Department of Earth Science Education, Seoul National University, Seoul, Korea 2 Research Institute of Oceanography, Seoul National University, Seoul, Korea Abstract Study Area and Data Conclusion Comparison with Numerical Model Simulation Spatial Distribution of Extreme SSTs Extreme value analysis (EVA) has been extensively used to understand and predict long-term return extreme values. This study provides the first approach to EVA using satellite-observed sea surface temperature (SST) data over the past decades. Representative EVA methods were compared to select an appropriate method to derive SST extremes of the East/Japan Sea (EJS). As a result, the peaks-over- threshold (POT) method showed better performance than the other methods. The Optimum Interpolation Sea Surface Temperature (OISST) database was used to calculate the 100- year-return SST values in the EJS. The calculated SST extremes were 1.60–3.44 °C higher than the average value of the upper 5th-percentile satellite-observed SSTs over the past decades (1982–2018). The monthly distribution of the SST extremes was similar to the known seasonal variation of SSTs in the EJS, but enhanced extreme SSTs exceeding 2 °C appeared in early summer and late autumn. The calculated 100-year-return SSTs were compared with the simulation results of the Coupled Model Intercomparison Project 5 (CMIP5) climate model. As a result, the extreme SSTs were slightly smaller than the maximum SSTs of the model data with a negative bias of –0.36 °C. This study suggests that the POT method can improve our understanding of future oceanic warming based on statistical approaches using SSTs observed by satellites over the past decades. ◀Figure. (A) Shape parameter (k) and (B) modified scale parameter (σ*) with 95% confidence intervals for every value of the threshold (μ). The red dot indicates the 98th percentile of SSTs. Thresholds for POT • In this study, the 98th-percentile value was selected as the threshold by checking the change and significant interval of the shape (k) and scale (σ) parameters according to the threshold setting. • We selected the thresholds marked by red dots. These values have also been used in previous studies (Parey et al., 2007; Laurent and Parey, 2007; Cheng et al., 2014), which compute the extreme values of surface air temperatures. A similar approach was applied to calculate the extreme values of precipitation (Cheng and AghaKouchak, 2014). Monthly Distribution of Extreme SSTs ▲Figure. Examples for the computed result of the monthly extreme SST. (A-L) Distribution of the probability of monthly SST values, where the red line represents the fitted distribution for the computation of the extreme SST by the POT method and (M) Q-Q plot for all months. ▲ Figure. Spatial distribution of (A) monthly 100-year return SSTs using the POT method and (B) temperature differences between the computed SST extremes and mean values of satellite-observed SSTs (1982 – 2018) within the upper 5th percentile. ▲Figure. (A) Bathymetry of the Northwestern Pacific, where the red box shows the study area, and (B) spatial distribution of sea surface temperatures (SSTs) averaged for the period from 1982 to 2018 in the study area. Satellite-observed Sea Surface Temperature Data • The daily Optimal Interpolation SST (OISST) data • provided by the National Oceanic and Atmospheric Administration (NOAA)/Oceanic and Atmospheric Research (OAR)/Earth System Research Laboratory (ESRL) Physical Sciences Lab (PSL) • based on NOAA/AVHRR data the longest period from 1981 onwards • The data used in this study covered the period from 1982 to 2018. Simulated Sea Surface Temperature Data of Climate Model • The Coupled Model Intercomparison Project 5 (CMIP5) monthly data on single-level • extracted from global ocean data from Dec. 2005 to Nov. 2099 • obtained from the Representative Concentration Pathway (RCP) 4.5 experiment using the HadGEM2- ES model (UK Met Office, UK) St