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To examine the characteristics of future precipitation under climate change is of great significance to urban water security. In this paper, multiple machine learning techniques, i.e., statistical downscaling model (SDSM), support vector machine (SVM), and multilayer perceptron (MLP), were used to downscale large-scale climatic variables simulated by the General Circulation Models (GCMs) to precipitation on a local scale. It was demonstrated in Shenzhen city, China, through multisite downscaling schemes based on projections from the Max Planck Institute Earth System Model (MPI-ESM1.2-HR), Meteorological Research Institute Earth System Model Version 2.0 (MRI-ESM2.0), and Beijing Climate Center Climate System Model (BCC-CSM2-MR). The obtained results showed that the downscaled precipitation would provide good monthly simulations against observations at 10 discrete stations. Regardless of superior performance of SVM and MLP over SDSM, the daily precipitation simulations should be further improved, and downscaling of heavy daily precipitations would be promoted by quantile mapping corrections. Due to the relatively poor simulation performance of BCC-CSM2-MR, the other two climate models were considered under the Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios) for ensemble precipitation projections for 2015–2100. Under the SSP1-2.6 scenario, the amounts of annual average precipitation for 10 stations were estimated to be higher relative to the historical period (2.7%–17%), and 9 out of 10 stations presented an increasing trend. However, downward trends also existed at three stations when it comes to scenarios SSP2-4.5 and SSP5-8.5. Moreover, a significantly positive trend was found to dominate the trend changes of annual extreme daily precipitation during 2015–2050, but the detected trends at stations were greatly dependent on the downscaling techniques and climate models. Besides, the increase in daily extreme precipitations for various return periods as well as statistically different precipitation characteristics for discrete stations would further shed light on urgent demands on urban resilient strategies for climate change adaptation. [ABSTRACT FROM AUTHOR]

This paper selected the representativephreatic water lever data of 5 monitoring stations in Taiyuan alluvial plain area. It identified those data’s trend, persistence and mutation by using linear trend estimation and Mann-Kendall mutation test method. The results show that the groundwater level in Chengbei and Xigu stations is less fluctuated, while the water levels of Nangunian, Dajingyu and Lianglong stations are higher; the trends of groundwater level are declining in Dajingyu, Chengbei and Xigu stations and the decreasing rate is 0. 49 m/ a in Dajingyu where the rate is the greatest; the least decreasing rate is 0. 02 m/ a in Chengbei, while it is 0. 36 m/ a in Lianglong Station; Nangunian and Xigu stations show rising trend with the rate of 1. 09 m/ a and 0. 06 m/ a respectively; the M-K mutation test shows that the decline trend of Lianglong Station is significant, while the trends in the rest of stations are not significant. The mutation times in Nangunian, Dajingyu and Xigu stations occur in 2006, 2002 and 2003 respectively, while Chengbei and Lianglong stations have not mutation points. [ABSTRACT FROM AUTHOR]