On the basis of daily precipitation records at 76 meteorological stations in the arid region, northwest of China, the spatial and temporal distribution of mean precipitation and extremes were analysed during 1960–2010. The Mann–Kendall trend test and linear least square method were utilized to detect monotonic trends and magnitudes in annual and seasonal mean precipitation and extremes. The results obtained indicate that both the mean precipitation and the extremes have increased except in consecutive dry days, which showed the opposite trend. The changes in amplitude of both mean precipitation and extremes show seasonal variability. On an annual basis, the number of rain days (R0.1) has significantly increased. Meanwhile, the precipitation intensity as reflected by simple daily intensity index (SDII), number of heavy precipitation days (R10), very wet days (R95p), max 1‐day precipitation amount (RX1day) and max 5‐day precipitation amount (RX5day) has also significantly increased. This suggests that the precipitation increase in the arid region is due to the increase in both precipitation frequency and intensity. Trends in extremes are very highly correlated with mean trends of precipitation. The spatial correlation between trends in extremes and trends in the mean is stronger for winter (DJF) than for annual and other seasons. The regional annual and seasonal precipitation and extremes are observed the step jump in mean in the late 1980s. Overall, the results of this study are good indicators of local climate change, which will definitely enhance human mitigation to natural hazards caused by precipitation extremes. Copyright © 2012 John Wiley & Sons, Ltd.

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Drought occurs more frequently in the context of climate change and threatens water security worldwide. An appropriate fitting distribution is crucial for accurately identifying drought using drought indices. Here, seven two-parameter distributions (Gamma, Gumbel, Logistic, Log-Logistic, Log-normal, Normal, and Weibull) and four distributions (general logistic, generalized extreme value, Normal, and Pearson Type III) are applied to calculate the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI), respectively, to choose the most appropriate fit for the 541 stations in China. The results show that, in most cases, Gamma and general logistic were the best distributions for SPI and SPEI, respectively. Nevertheless, we noted that Pearson Type III and generalized extreme value also fit the SPEI series well, indicating the importance of distribution fitting assessment for various regions. The annual and seasonal drought evolutions across China were clarified, with drought decreasing significantly in western Northwest China (annually and each season), northern North China (spring and winter), the Tibetan Plateau (spring), and the lower reaches of the Yangtze River (winter) and increasing mainly in South China (spring) and western South China (summer and autumn). The intensity, duration and severity of light, moderate, and severe drought were also detected; the results suggest that drought in China is mainly concentrated around the changes in light and moderate drought. Additionally, we assessed the sensitivity of drought evolution to various meteorological variables. The most sensitive variable in South China, North China and the Tibetan Plateau is precipitation, followed by wind speed, temperature, relative humidity, and sunshine duration; in Northwest China, the order is wind speed, precipitation, temperature, relative humidity, and sunshine duration. Furthermore, the correlation results also indicate that the drought evolution is affected by the multivariate ENSO index, while the influencing characteristics are different in various seasons and regions. The results in this paper contribute to drought mitigation and the effective utilization of water resources in China.

Based on a temperature anomaly time series from 16 international exchange stations in Xinjiang from 1957 to 2012, the multi-scale characteristics of temperature variability were analysed using the ensemble empirical mode decomposition (EEMD) method. Regional differences in variation trends and change-points were also preliminarily discussed. The results indicated that in the past 50+ years, the overall temperature in Xinjiang has exhibited a significant nonlinear upward trend, and its changes have clearly exhibited an inter-annual scale (quasi-3 and quasi-6-year) and inter-decadal scale (quasi-10 and quasi-30-year). The variance contribution rates of each component demonstrated that the inter-annual change had a strong influence on the overall temperature change in Xinjiang, and the reconstructed inter-annual variation trend could describe the fluctuation state of the original temperature anomaly during the study period. The reconstructed inter-decadal variability revealed that the climate mode in Xinjiang had a significant transformation before and after 1995, namely the temperature anomaly shift from a negative phase to a positive one. Furthermore, there were regional differences in the nonlinear changes and change-points of temperature. At the same time, the results also suggested that the EEMD method can effectively reveal variations in long-term temperature sequences at different time scales and can be used for the complex diagnosis of nonlinear and non-stationary signal changes.

Based on the hydrological and meteorological data in the headwater region of the Kaidu River during 1960-2009, the multi-scale characteristics of runoff variability were analyzed using the ensemble empirical mode decomposition method (EEMD), and the aim is to investigate the oscillation mode structure characteristics of runoff change and its response to climate fluctuation at different time scales. Results indicated that in the past 50 years, the overall runoff of Kaidu River in Xinjiang has showed a significant nonlinear upward trend, and its changes have obviously exhibited an interannual scale (quasi-3 and quasi-6-year) and inter-decadal scale (quasi-10 and quasi-25-year). Variance contribution rates of each component manifested that the inter-decadal change had been playing a more important role in the overall runoff change for Kaidu River, and the reconstructed interannual variation trend could describe the fluctuation state of the original runoff anomaly during the study period. The reconstructed inter-decadal variability effectively revealed that the runoff for Kaidu River changed over the years, namely the states of abundance and low water period appear alternately. In addition, we found that runoff has a positive correlation to precipitation and temperature at different time scales, but they are most significant and relevant at inter-decadal scale, indicating the inter-decadal scale is most suitable for investigating the responses of runoff dynamics to climate fluctuation. At the same time, the results also suggested that EEMD is an effective method to analyze the multi-scale characteristics of nonlinear and non-stationary signal.