Abstract Climate indices characterize climate systems and may identify important indicators for long-term precipitation, which are driven by climate interactions in atmosphere-ocean circulation. In this study, we investigated the climate indices that are effective indicators of long-term precipitation in South Korea, and examined their relationships based on statistical methods. Monthly total precipitation was collected from a total of 60 meteorological stations, and they were decomposed by ensemble empirical mode decomposition (EEMD) to identify the inherent oscillating patterns or cycles. Cross-correlation analysis and stepwise variable selection were employed to select the significant climate indices at each station. The climate indices that affect the monthly precipitation in South Korea were identified based on the selection frequencies of the selected indices at all stations. The NINO12 indices with four- and ten-month lags and AMO index with no lag were identified as indicators of monthly precipitation in South Korea. Moreover, they indicate meaningful physical information (e.g. periodic oscillations and long-term trend) inherent in the monthly precipitation. The NINO12 indices with four- and ten- month lags was a strong indicator representing periodic oscillations in monthly precipitation. In addition, the long-term trend of the monthly precipitation could be explained by the AMO index. A multiple linear regression model was constructed to investigate the influences of the identified climate indices on the prediction of monthly precipitation. Three identified climate indices successfully explained the monthly precipitation in the winter dry season. Compared to the monthly precipitation in coastal areas, the monthly precipitation in inland areas showed stronger correlation to the identified climate indices.

中文翻译：

---

使用集合经验模式分解识别韩国气候指数与长期降水之间的关系

摘要 气候指数表征气候系统，可以确定由大气-海洋环流中的气候相互作用驱动的长期降水的重要指标。在这项研究中，我们调查了作为韩国长期降水有效指标的气候指数，并基于统计方法检查了它们之间的关系。从总共 60 个气象站收集每月总降水量，并通过集合经验模式分解（EEMD）对它们进行分解，以确定固有的振荡模式或周期。采用互相关分析和逐步变量选择来选择每个站点的显着气候指数。影响韩国月降水量的气候指数是根据所有站点所选指数的选择频率确定的。将具有 4 个月和 10 个月滞后的 NINO12 指数和没有滞后的 AMO 指数确定为韩国的月降水指标。此外，它们表明了每月降水固有的有意义的物理信息（例如周期性振荡和长期趋势）。具有 4 个月和 10 个月滞后的 NINO12 指数是代表月降水周期性振荡的有力指标。此外，月降水量的长期趋势可以用AMO指数来解释。构建多元线性回归模型来研究已识别的气候指数对月降水预测的影响。三个确定的气候指数成功地解释了冬季旱季的月降水量。与沿海地区的月降水量相比，内陆地区的月降水量与确定的气候指数的相关性更强。