Using a Hidden Markov Model to Analyze the Flood-Season Rainfall Pattern and Its Temporal Variation over East China

The homogeneous hidden Markov model (HMM), a statistical pattern recognition method, is introduced in this paper. Based on the HMM, a 53-yr record of daily precipitation during the flood season (April–September) at 389 stations in East China during 1961–2013 is classified into six patterns: the South China (SC) pattern, the southern Yangtze River (SY) pattern, the Yangtze–Huai River (YH) pattern, the North China (NC) pattern, the overall wetter (OW) pattern, and the overall drier (OD) pattern. Features of the transition probability matrix of the first four patterns reveal that 1) the NC pattern is the most persistent, followed by the YH, and the SY is the least one; and 2) there exists a SY–SC–SY–YH–NC propagation process for the rain belt over East China during the flood season. The intraseasonal variability in the occurrence frequency of each pattern determines its start and end time. Furthermore, analysis of interdecadal variability in the occurrence frequency of each pattern in recent six decades has identified three obvious interdecadal variations for the SC, YH, and NC patterns in the mid–late 1970s, the early 1990s, and the late 1990s. After 2000, the patterns concentrated in the southern region play a dominant role, and thus there maintains a “flooding in the south and drought in the north” rainfall distribution in eastern China. In summary, the HMM provides a unique approach for us to obtain both spatial distribution and temporal variation features of flood-season rainfall.