Extended Abstract

Statistical Prediction of China Summer rainfall Using RegressionXingang Dai 1 Zhi Cheng 2 Ping Wang 3RCE-TEA 1 , Institute of Atmospheric Physics, Beijing 100029, Lanzhou University 2 ,Lanzhou 730000, Chinese Academy of Meteorological Sciences 3 , Beijing 100081daixg@tea.ac.cn, chengzhi@tea.ac.cnThis study focuses on inter-seasonal weather prediction for China usingstatistical methods. The interaction between East Asian monsoon and westerlyis one of the key mechanisms that det ermining major character of summerclimate pattern in China. Many studi es have found se veral important physicalfactors that exert consi derable imp acts on the su mmer climate, which areENSO cycle, the North Atlantic oscill ation (NAO) and spring snow cover overTibetan Plateau. A linear r egression equation consisting of the factors issuggested to make summer rainfall pred iction. The ENSO cycle is describedby two spring SSTA trends referring developing and decaying phases of an ElNino event , respectively , corresponding to quite dif ferent climate p attern inEast China. Severe fl oods often appear in southern China in sec ond phase,and a dry climate usually appears in the north of China in the first phase. Aninverse pattern of the summer rainfall again st El Nino is the characteristics ofLa Nina ev ent. On the other hand, anomalous westerly in mid-latitudes is inconnection with NAO phase which infl uences the quasi-st ationary waves onthe zonal flow and blocking loc ation and frequency being correlated to thesummer rainfall pattern Besides, previous spring snow cover over the plateaucould influence the coming summer rainfall . Statistics revealed that the morethe snow cover, the more the rainfall along Changjiang River valley, and viceversa. There are two approaches in predicting the rainfall pattern. The first is topredict the East Asian M onsoon index, by which the predicted rainfall field ismade basing on regression method. The sec ond is directly to predict regionalrainfall by the regression equation. A nd the regional predictions are thendistributed to every weather st ation according to recent st atistics between thestation and regional rainfalls. The st ation or regional pr ediction is thenemployed to estimate extreme weather event according to ‘Extreme V alueTheory” (GEV) in our case study . Finally, statistical conditions ar e discussedfor the predictors and expected prediction.-394-