Increase of Future Summer Rainfall in the Middle and Lower Reach of the Yangtze River Basin Projected With a Nonhomogeneous Hidden Markov Model

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