The River Aksu is the principal tributary to the River Tarim, providing about three quarters of its discharge. It originates in Kyrgyzstan and flows into the arid areas of the Xinjiang Uyghur Autonomous Region in China, where an extensive irrigated agriculture has been developed in the river oases. The aim of the present contribution is to review the current trends in temperature, precipitation, and river discharge and links between these variables. The temperature in the region and the river discharge have been rising. Changes were studied using multiple trend analyses with different start and end years. Correlations between daily temperature and discharge are high and statistically significant for two headwater subcatchments of the Aksu for most of the time. However, there are episodes in late summer or beginning of autumn when correlations between temperature and discharge for the Xiehela station are absent. This can only be explained by Glacial Lake Outburst Floods from the Lake Merzbacher that are not routinely monitored. On an annual time scale, changes in summer discharge in the highly glacierized Xiehela subcatchment are dominated by changes in temperature. In contrast, in the subcatchment Shaliguilanke, variations in summer streamflow are more strongly influenced by variations in precipitation. A comparison of links between climatic variables and streamflow at different temporal scales is offered. Perspectives for seasonal forecasting are examined.

Summer rainfall in the Yangtze River basin is predicted using neural network techniques. Input variables (predictors) for the neural network are the Southern Oscillation Index (SOI), the East Atlantic/Western Russia (EA/WR) pattern, the Scandinavia (SCA) pattern, the Polar/Eurasia (POL) pattern and several indices calculated from sea surface temperatures (SST), sea level pressures (SLP) and snow data from December to April for the period from 1993 to 2002. The output variable of the neural network is rainfall from May to September for the period from 1994 to 2002, which was previously classified into six different regions by means of a principal component analysis (PCA). Rainfall is predicted from May to September 2002.The winter SST and SLP indices are identified to be the most important predictors of summer rainfall in the Yangtze River basin. The Tibetan Plateau snow depth, the SOI and the other teleconnection indices seem to be of minor importance for an accurate prediction. This may be the result of the length of the available time series, which does not allow a deeper analysis of the impact of multi‐annual oscillations.The neural network algorithms proved to be capable of explaining most of the rainfall variability in the Yangtze River basin. For five out of six regions, our predictions explain at least 77% of the total variance of the measured rainfall. Copyright © 2007 Royal Meteorological Society