We applied the Mann-Kendall (MK) test and Bayesian model to systematically explore trends and abrupt changes of the precipitation series in the Pearl River basin. The results showed that no significant trends were detected for annual precipitation and summer or winter precipitation totals. Significant negative trends were identified for the number of rainy days across the Pearl River basin; significant positive trends were observed regarding precipitation intensity (PI). In particular, the precipitation totals and frequencies of extremely high precipitation events are subject to significant positive trends. In addition, the number of extremely low precipitation events was also increasing significantly. Factors affecting the changes in precipitation patterns are the weakening Asian monsoon and consequently increasing moisture transport to Southern China and the Pearl River basin. In summary, the main findings of this study are: (1) increased precipitation variability and high-intensity rainfall was observed though rainy days and low-intensity rainfall have decreased, and (2) the amount of rainfall has changed little but its variability has increased over the time interval divided by change points. These finds indicate potentially increased risk for both agriculture and in locations subject to flooding, both urban and rural, across the Pearl River basin.

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Abstract. Daily precipitation series at station or local scales is a critical input for rainfall-runoff modelling which, in turn, plays a vital role in the assessment of climate change impact on hydrologic processes and many other water resource studies. Future climate projected by General Circulation Models (GCMs) presents averaged values in large scales. Therefore, downscaling techniques are usually needed to transfer GCM-derived climate outputs into station-based values. In this study, a statistical downscaling model is investigated and its applicability in generating daily precipitation series is tested in the subtropical region of South China, which has not been investigated before. The model includes the first-order Markov chain for modeling wet day probability, Gamma distribution function for describing variation of wet-day precipitation amounts, and a statistical downscaling approach to transferring large-scale (in both space and time) future precipitation series from GCM climate change scenarios to station or local scales. A set of observed daily precipitation series of 32 years from 17 rainfall stations in and around a grid of 2.5° in latitude by 3.75° in longitude in Guangdong province of China is used to evaluate the model accuracy and validate the downscaling results. The downscaled daily precipitation series and the extreme precipitation features (including maximum, maximum 3-day average and maximum 7-day average) are compared with the observed values. The results show that the proposed model is capable of reproducing the mean daily amount and model parameters of the daily precipitation series at station or local scales in the study region.

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