Abstract
AbstractAmong 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
