Among the statistical downscaling tools available for regional climate simulation, the nonhomogeneous hidden Markov model (NHMM) is a powerful and efficient algorithm. It consists of establishing statistical relations between predictand and predictors through a hidden layer of Markov process and a covariate nonhomogeneous term as external forcing. Here, it is used to simulate summer daily precipitation in the middle and lower reach of the Yangtze River basin (MLYRB), including future projection. Results show that NHMM well identifies large-scale circulation features that are physically consistent with regional rainfall patterns. MLYRB exhibits a general wetness for 1.5°C and 2°C global warming targets, with smaller (larger) increase for the west (east). Such changes correspond to increases in the occurrence frequency of synoptic weather patterns with stronger and more westward Western Pacific Subtropical High and stronger westerly jet. This contributes to moistening MLYRB with increasing occurrence frequency for those wetter rainfall patterns.