Based on the Multi-Scale Standardized Precipitation Index （MSPI）, extreme severe drought events in China during 1961-2010 were identified, and the seasonal, annual, and interdecadal variations of the cluster- ing extreme drought events were investigated by using the spatial point process theory. It is found that severe droughts present a trend of gradual increase as a result of the significant increase and clustering tendency of severe droughts in autumn. The periodicity analysis of the clustering extreme droughts in different seasons suggests that there is a remarkable interdecadal change in the occurrence of clustering extreme droughts in winter. Meanwhile, it is revealed that the clustering extreme drought events exhibit greatly different annual mean spatial distributions during 1961 2010, with scattered and concentrated clustering zones alternating on the decadal timescale. Furthermore, it is found that the decadal-mean spatial distributions of extreme drought events in summer are correlated out of phase with those of the rainy bands over China in the past 50 years, and a good decadal persistence exists between the autumn and winter extreme droughts, implying a salient feature of consecutive autunm-winter droughts in this 50-yr period. Compared with other regions of China, Southwest China bears the most prominent characteristic of clustering extreme droughts.
By establishing the Markov model for a long-range correlated time series （LRCS） and analysing its evolutionary characteristics, this paper defines a physical effective correlation length （ECL） T, which reflects the predictability of the LRCS. It also finds that the ECL has a better power law relation with the long-range correlated exponent γ of the LRCS： T = Kexp（-γ/0.3） ＋ Y, （0 〈 γ〈 1） the predictability of the LRCS decays exponentially with the increase of γ It is then applied to a daily maximum temperature series （DMTS） recorded at 740 stations in China between the years 1960-2005 and calculates the ECL of the DMTS. The results show the remarkable regional distributive feature that the ECL is about 10-14 days in west, northwest and northern China, and about 5-10 days in east, southeast and southern China. Namely, the predictability of the DMTS is higher in central-west China than in east and southeast China. In addition, the ECL is reduced by 1-8 days in most areas of China after subtracting the seasonal oscillation signal of the DMTS from its original DMTS; however, it is only slightly altered when the decadal linear trend is removed from the original DMTS. Therefore, it is shown that seasonal oscillation is a significant component of daily maximum temperature evolution and may provide a basis for predicting daily maximum temperatures. Seasonal oscillation is also significant for guiding general weather predictions, as well as seasonal weather predictions.
