中文摘要：

将去趋势波动分析法（detrended fluctuation analysis,DFA）和替代数据法相结合,同时引入启发式分割算法和卡方检验,提出了一种确定极端气候事件阈值的新方法,称为随机重排去趋势波动分析（stochastic sort detrendedfluctuation Analysis,S-DFA）方法.该方法的基本物理思想是认为资料序列中出现概率非常小的数据点属于小概率事件,这些数据点包含的系统整体演化信息极少,它们所对应的状态是系统演化的异常状态或是系统受到外界扰动而出现的极端状态;而出现概率密度较大或者概率分布比较均匀的数据点则不属于小概率事件的范畴,这些数据点包含了系统演化的丰富信息,是系统演化的正常状态.同百分位阈值方法相比,S-DFA方法明确指出了极端事件和非极端事件之间的临界值,并通过数值试验从不同的角度对S-DFA方法进行检验,以验证S-DFA方法的有效性.

英文摘要：

By combining detrended fluctuation analysis（DFA） method with surrogate data method,and using the Heuristic segmentation algorithm as well as Chi-Square statistics,we develop a new method to determine the threshold of extreme events,e.g.stochastically re-sorting detrended fluctuation analysis（S-DFA） method.The S-DFA method has a certain phsical background,when the occurrence rate of the data is small,then these data belong to little-probability events and they contain so little information about the dynamic system,the states corresponding to these data are abnormal states or extreme states of the system.When the occurrence rate of the data is large or even in distribution these data do not belong to little-probability events and they contain much information about the system,the states corresponding to these data are normal states of the system.Compared with the Percentile curves method,the S-DFA method gives the critical value between extreme event and non-extreame event,which is definite and unique.We also extensively validate the effectiveness of S-DFA method through extreme event detection.