The predictability of daily rainfall during rainy season over East Asia by a Bayesian nonhomogeneous hidden Markov model

Precipitation plays a significant role in human society and the environment, and how large-scale climatic features influence precipitation has obtained worldwide attention. The nonhomogeneous hidden Markov model (NHMM) is a typical method to downscale large-scale climatic elements to the regional level for many hydrologic applications. The traditional NHMM using point estimates of parameters has the risk to have no solutions for parameter estimation, but the Bayesian-NHMM provides a Bayesian method to estimate parameters and avoids the risk. In this study, the suitability of the Bayesian-NHMM in East Asia is evaluated. Two typical regions (i.e., the Yangtze River basin and the Zhujiang River basin in China) dominated by different climates were chosen to check model performance. Results show that: (1) the model could divide rainy-season precipitation into several hidden states, whose variation is correspondent to the variability of monsoon and flux moisture transportation; (2) the model captures seasonality and inter-annual variation of precipitation amount and also wet days during rainy season well in both river basins. These results suggest that the model could improve the prediction of daily precipitation in East Asia, which in turn could help many regions with similar climatic conditions worldwide to supervise floods and droughts.