Quantifying Interannual Covariability between Precipitation over Eastern China and SST in the Indo-Pacific Regions

Signal separation and spatiotemporal reconstruction of multi-time-scale climate fields are conducted based on the multitaper method-singular value decomposition (MTM-SVD) method in this study, with a focus on interannual precipitation responses over eastern China to sea surface temperature (SST) forcing of the Indo-Pacific region at a specific cycle and the related mechanisms illustrated. Results show that the precipitation and SST exhibit two synergistic interannual cycles: the quasi-biennial (QB) (2.4-yr) and quasi-quinquennial (QQ) (4.7-yr) cycles. These modes are influenced by multiple atmospheric responses associated with the central Pacific and eastern Pacific types of El Ni & ntilde;o, including the Pacific-Japan/East Asia-Pacific teleconnection, western North Pacific subtropical high, and western North Pacific anomalous anticyclone. On the QB cycle, precipitation over South China correlates positively with the QB component of El Ni & ntilde;o. On the QQ cycle, the meridional tripole precipitation pattern is dominated during the mature stage of the QQ component associated with El Ni & ntilde;o. The combined effects of these two modes are demonstrated for the 1997/98 precipitation events, highlighting regional differences in the relative contributions of QB and QQ variabilities. During the winter of 1997, the increased precipitation over South China is explained by the collective effects of the QB and QQ variabilities. During the summer of 1998, the decreased precipitation south of the Yangtze River is attributed to the QB variability, and this effect is mitigated by the QQ variability; the increased precipitation north of the Yangtze River is attributed to the combined effects of the QB and QQ variabilities. This research aims to separate the impacts and mechanisms associated with the Indo-Pacific SST forcing on precipitation over China at interannual scales, providing scientific guidance for corresponding climate predictions.