Machine learning-based 1-year-lead summer rainfall prediction over the DPRK using regionally optimized spatial-contrast indices

Abstract

The northern part of the Korean Peninsula (NPKP) is a region highly dependent on summer monsoon precipitation for water resources and disaster risk management, and reliable one-year-ahead prediction of summer precipitation is pivotal for formulating climate adaptation strategies. Using the NCEP/NCAR reanalysis dataset spanning 75 years (1948–2022) and precipitation data from 37 meteorological stations in the Democratic People's Republic of Korea, this study developed regionally optimized spatial-contrast indices to explain the interannual variability of summer precipitation. The results show that the circulation patterns with high concurrent correlation with summer precipitation do not persist into the following year. However, the deepened low-pressure system over inland Eurasia, particularly around Lake Baikal, in the preceding summer (t−1) exhibits a significant lagged correlation with summer precipitation (t). Based on this physical insight, the study designed spatial-contrast indices that quantify the regional circulation anomaly differences between Siberia and eastern China. A back-propagation neural network (BPNN) trained on these indices achieved an anomaly correlation coefficient (ACC) of 0.59, a root mean square error (RMSE) of 134.61 mm, and a mean bias error (MBE) of −4.22 mm during the 5-fold cross-validation period (1949–2022), significantly outperforming traditional linear regression models. These findings demonstrate that regionally optimized atmospheric circulation indices provide robust predictive skill for long-lead precipitation forecasting in data-scarce regions.