Dynamical downscaling (DDS) was conducted over Japan by using a regional atmospheric model with reanalysis data to investigate the rainfall duration bias over Kyushu, Japan, in July and August from 2006 to 2015. The model results showed that DDS had a positive rainfall duration bias over Kyushu and a dry bias over almost all of Kyushu, which were emphasized for extreme rainfall events. Investigated was the rainfall duration bias for heavy rainfall days, accompanied by synoptic-scale forcing, in which daily precipitation exceeded 30 mm day−1 and covered over 20% of the Kyushu area. Heavy rainfall days were sampled from observed rainfall data that were based on rain gauge and radar observations. A set of daily climatic variables of horizontal wind and equivalent potential temperature at 850 hPa and sea level pressure, around southwestern Japan, corresponding to the sampled dates, was selected to conduct a self-organizing map (SOM) and K-means method. The SOM and K-means method objectively classified three synoptic patterns related to heavy rainfall over Kyushu: strong monsoon, weak monsoon, and typhoon patterns. Rainfall duration had a positive bias in western Kyushu for the strong monsoon pattern and a positive bias in southern and east-coast Kyushu for the typhoon pattern, whereas there was little rainfall duration bias in the weak monsoon pattern. The bias for the typhoon pattern was related to rainfall events with a strong rainfall peak. The results suggest that bias correction for rainfall duration would be required for accurately estimating direct runoff in a catchment area in addition to the precipitation amount.

A novel method is proposed to create very long term daily precipitation data for the extreme statistics by computing very long term daily sea level pressure (SLP) with the SLP emulator (a statistical multilevel regression model) and then converting the SLP into precipitation by combining statistical downscaling methods of the analog ensemble and singular value decomposition (SVD). After a review of the SLP emulator, we present a multilevel regression model constructed for each month that is based on a time series of 1000 principal components of SLPs on global reanalysis data. Simple integration of the SLP emulator provides 100-yr daily SLP data, which are temporally interpolated into a 6-h interval. Next, the pressure–precipitation transmitter (PPT) is developed to convert 6-hourly SLP to daily precipitation. The PPT makes its first-guess estimate from a composite of time frames with analogous SLP transition patterns in the learning period. The departure of SLPs from the analog ensemble is then corrected with an SVD relationship between SLPs and precipitation. The final product showed a fairly realistic precipitation pattern, displaying temporal and spatial continuity. The annual-maximum precipitation of the estimated 100-yr data extended the tail of probability distribution of the 8-yr learning data.

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We examined forty-five typhoons associated with insurance losses in Japan to explicitly describe typhoon-related variables that explain insurance-loss variations.Multiple regression analysis revealed that the combination of maximum wind speed and translation speed explained more of the variation in insurance-loss size than what the regression model with maximum wind speed alone did. Using maximum wind speed and gale-area radius as explanatory variables also slightly improved the explained variance, but it was less stable than the multiple regression model combining maximum wind speed and translation speed. The translation speed suggested an inland expansion of the strongwind area associated with wind-speed asymmetry, while considering the exposure led to similar conclusions. Our regression model can be applied to estimate changes in the damage and uncertainty by adjusting the typhoon characteristics under multiple climatechange scenarios.

The sampling downscaling (SmDS) in which a regional atmospheric model is integrated for sampled periods was performed for summertime Hokkaido. Selected are top two and bottom two years of the general circulation model projection onto the first singular value decomposition mode where heavy precipitation in southern Hokkaido is correlated with the moisture flux convergence in the synoptic field. The SmDS result integrated for the four years successfully reproduces the dynamical downscaling for 30 years, in terms of climatological precipitation and the 99-percentile value of daily precipitation. This indicates that SmDS can be applied to the environment where local precipitation is mostly controlled by synoptic climate patterns. A further statistical consideration in this study supports the notion. It is also demonstrated that SmDS selects a group of years where extreme events likely occur another group of years where they rarely occur.