In this study,monthly NCEP/NCAR reanalysis data and NOAA ERSST as well as observed precipitation data from 160 stations in China were used to investigate coupled modes affecting the rainfall over China and sea surface temperature （SST） in the Pacific during boreal summertime based on singular value decomposition （SVD） method.The SVD analysis revealed three remarkable coupled modes：rainfall over North China associated with an ENSO-like SST pattern （ENSO-NC）,rainfall over the Yangtze River valley associated with SST anomalies in the western tropical Pacific （WTP-YRV）,and rainfall over the Yellow River loop valley associated with tropical Pacific meridional mode-like SST pattern （TPMM-YRLV）.These coupled SVD modes appear robust and closely correlated with the single field.Furthermore,the covariabilities among of the three coupled modes have different characteristics at the decadal time scale.In addition,the possible atmospheric teleconnections of the coupled rainfall and SST modes were discussed.For the ENSO-NC mode,anomalous low-pressure and high-pressure over the Asian continent induces moisture divergence over North China and reduces summer rainfall there.For the WTP-YRV mode,East Asia-Pacific teleconnection induces moisture convergence over the Yangtze River valley and enhances the summer rainfall there.The TPMM SST and the summer rainfall anomalies over the YRVL are linked by a circumglobal,wave-train-like,atmospheric teleconnection.
In this study, the relationship between El Nifio-Southern Oscillation （ENSO） and winter rainfall over Southeast China （SC） is demonstrated based on instrumental and reanalysis data. The results show that ENSO and SC winter rainfall （ENSO-SC rainfall） are highly correlated and intimately coupled through an anomalous high pressure over the northwestern Pacific. In mature phase, El Nifio （La Nina） events can cause more （less） rainfall over SC in winter. Due to the persistence and spring barrier of ENSO, SC winter rainfall has potential predictability of about half a year ahead with ENSO as a predictor.
