Analysis of precipitation characteristics of South and North China based on the power-law tail exponents

Precipitation sequence is a typical nonlinear and chaoticobservational series, and studies on precipitation forecasts arerestricted to the use of traditional linear statistical methods,especially when analysing the regional characteristics ofprecipitation. In the context of 20 stations' daily precipitationseries (from 1956 to 2000) in South China (SC) and North China (NC),we divide each precipitation series into many self-stationarysegments by using the heuristic segmentation algorithm (briefly BGalgorithm). For each station's precipitation series, we calculatethe exponent of power-law tail (EPT) of the cumulative probabilitydistribution of segments with a length larger than $l$ forprecipitation and temperature series. Our results show that thepower-law decay of the cumulative probability distribution ofstationary segments might be a common attribution for precipitationand other nonstationary time series; the EPT somewhat indicates theprecipitation duration and its spatial distribution that might bedifferent from area to area. The EPT in NC is larger than in SC;Meanwhile, EPT might be another effective way to study the abruptchanges in nonlinear and nonstationary time series.