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Analyses of the variation characteristics for aridity index (AI) can further enhance the understanding of climate change and have effect on hydrology and agriculture. In this paper, based on the data of 283 standard meteorological stations, the temporal-spatial variations and the influences of climate factors on AI were investigated and the relationship between AI and two climate indices (the Arctic Oscillation (AO); El Nino-Southern Oscillation (ENSO)) were also assessed in northern China (NC) during the period from 1961 to 2012. The results revealed that the annual mean AI decreased at the rate of −0.031 per decade in the past 52 years and the trend was statistically significant at the 0.01 level. The Mann-Kendall (M-K) test presented that the percentages of stations with positive trends and negative trends for AI were 10 and 81.9 % (22.6 % statistically significant), respectively. Spatially, in the western part of 100° E, the extremely dry area declined and the climate tended to become wet obviously. In the eastern part of 100° E, dry area moved toward the east and the south, which resulted in the enhancement of semiarid area and the shrinkage of subhumid area. The contributions of sunshine duration and precipitation to the decline of AI are more than those of other meteorological variables in NC. Moreover, the average temperature has risen significantly and AI decreased in NC, which indicated the existence of 'paradox.' Relationship between climate indices (AO and ENSO) and AI demonstrated that the influence of ENSO on AI overweight the AO on AI in NC. [ABSTRACT FROM AUTHOR]

In this paper, a statistical inference of Southeastern Canada extreme daily precipitation amounts is proposed using a classical nonstationary peaks-over-threshold model. Indeed, the generalized Pareto distribution (GPD) is fitted to excess time series derived from annual averages of independent precipitation amount events above a fixed threshold, the 99th percentile. Only the scale parameter of the fitted distribution is allowed to vary as a function of a covariate. This variability is modeled using B-spline function. Nonlinear correlation and cross-wavelet analysis allowed identifying two dominant climate indices as covariates in the study area, Arctic Oscillation (AO) and Pacific North American (PNA). The nonstationary frequency analysis showed that there is an east-west behavior of the AO index effects on extreme daily precipitation amounts in the study area. Indeed, the higher quantiles of these events are conditional to the AO positive phase in Atlantic Canada, while those in the more southeastern part of Canada, especially in Southern Quebec and Ontario, are negatively related to AO. The negative phase of PNA also gives the best significant correlation in these regions. Moreover, a regression analysis between AO (PNA) index and conditional quantiles provided slope values for the positive phase of the index on the one hand and the negative phase and on the other hand. This statistic allows computing a slope ratio which permits to sustain the nonlinear relation assumption between climate indices and precipitation and the development of the nonstationary GPD model for Southeastern Canada extremes precipitation modeling. [ABSTRACT FROM AUTHOR]