A physical vegetation model [the Two-Layer Model (2LM)] was applied to estimate the climate change impacts on rice leaf wetness (LW) as a potential indicator of rice blast occurrence. Japan was used as an example. Dynamically downscaled data at 20-km-mesh resolution from three global climate models (CCSM4, MIROC5, and MRI-CGCM3) were utilized for present (1981-2000) and future (2081-2100) climates under the representative concentration pathway 4.5 scenario. To evaluate the performance of the 2LM, the LW and other meteorological variables were observed for 108 days during the summer of 2013 at three sites on the Pacific Ocean side of Japan. The derived correct estimation rate was 77.4%, which is similar to that observed in previous studies. Using the downscaled dataset, the changes in several precipitation indices were calculated. The regionally averaged ensemble mean precipitation increased by 6%, although large intermodel differences were found. By defining a wet day as any day in which the daily precipitation was > 1 mm day-1, it was found that the precipitation frequency decreased by 6% and the precipitation intensity increased by 11% for the entire area. The leaf surface environment was estimated to be dry; leaf wetness, wet frequency, and wet times all decreased. It was found that a decrease in water trap opportunities due to reduced precipitation frequency was the primary contributor to the LW decrease. For blast fungus, an increased precipitation intensity was expected to enhance the washout effect on the leaf surface. In the present case, the infection risk was estimated to decrease for Japan.
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