In this study,changes in daily weather states were treated as a complex Markov chain process,based on a continuous-time watershed model(soil water assessment tool,SWAT) developed by the Agricultural Research Service at the U.S.Department of Agriculture(USDA-ARS).A finer classification using total cloud amount for dry states was adopted,and dry days were classified into three states:clear,cloudy,and overcast(rain free).Multistate transition models for dry-and wet-day series were constructed to comprehensively downscale the simulation of regional daily climatic states.The results show that the finer,improved,downscaled model overcame the oversimplified treatment of a two-weather state model and is free of the shortcomings of a multistate model that neglects finer classification of dry days(i.e.,finer classification was applied only to wet days).As a result,overall simulation of weather states based on the SWAT greatly improved,and the improvement in simulating daily temperature and radiation was especially significant.
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