ABSTRACT Summer precipitation over the northern part of the Korean Peninsula (SP‐NPKP) is critical for water resources, agriculture, and disaster prevention. This study aims to detect suitable atmospheric circulation indices for annual prediction of SP‐NPKP and to evaluate their predictive skill. We used 77 years of data from 1948 to 2024, including NCEP/NCAR reanalysis variables and observed summer precipitation from 37 stations. The study is based on the finding that 1‐year lag correlations between selected indices and SP‐NPKP generally exceed concurrent correlations. We analyzed linear trends of SP‐NPKP, sea level pressure over Asia, 700‐hPa vorticity anomalies, and Arctic Oscillation indices. Using the ‘area shift’ experiment, we identified optimal domains for sea level pressure anomalies over Asia and the North Pacific, yielding effective predictors: the SLP anomaly index over central Eurasia (SLPAI‐1), that over the Okhotsk Sea (SLPAI‐2), the 700‐hPa vorticity anomaly index (VORAI‐700), the 500‐hPa temperature anomaly index (TAI‐500), and the leading SLP principal component (SLP‐PC1). Annual predictions were performed using principal component regression (PCR) and backpropagation neural network (BPNN) models. Based on 5‐fold cross‐validation, PCR showed limited skill with R 2 = 0.1628, RMSE = 151.57 mm, and MAE = 124.22 mm, while BPNN demonstrated significantly superior performance with R 2 = 0.4031, RMSE = 119.81 mm, and MAE = 103.15 mm. This confirms that neural networks better capture the nonlinear dynamics of regional precipitation. Our study provides a novel, data‐driven framework for identifying region‐specific predictors, offering valuable insights for improving operational seasonal prediction systems in East Asia.
Ham et al. (Sat,) studied this question.
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