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[1] Bias-correction methods applied to monthly temperature and precipitation data simulated by multiple General Circulation Models (GCMs) are evaluated in this study. Although various methods have been proposed recently, an intercomparison among them using multiple GCM simulations has seldom been reported. Moreover, no previous methods have addressed the issue how to adequately deal with the changes of the statistics of bias-corrected variables from the historical to future simulations. In this study, a new method which conserves the changes of mean and standard deviation of the uncorrected model simulation data is proposed, and then five previous bias-correction methods as well as the proposed new method are intercompared by applying them to monthly temperature and precipitation data simulated from 12 GCMs in the Coupled Model Intercomparison Project (CMIP3) archives. Parameters of each method are calibrated by using 1948-1972 observed data and validated in the 1974-1998 period. These methods are then applied to the GCM future simulations (2073-2097) and the bias-corrected data are intercompared. For the historical simulations, negligible difference can be found between observed and bias-corrected data. However, the differences in future simulations are large dependent on the characteristics of each method. The new method successfully conserves the changes in the mean, standard deviation and the coefficient of variation before and after bias-correction. The differences of bias-corrected data among methods are discussed according to their respective characteristics. Importantly, this study classifies available correction methods into two distinct categories, and articulates important features for each of them.Citation: Watanabe, S., S. Kanae, S. Seto, P. J.-F. Yeh, Y. Hirabayashi, and T. Oki (2012), Intercomparison of bias-correction methods for monthly temperature and precipitation simulated by multiple climate models,

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Japan has seen an increase in the incidents of financial frauds over the last couple of decades. Although authorities are aware of the problem, an effective solution eludes them as fraudsters use innovative swindling methods and continually change the target group. Using a nationwide survey conducted by Hiroshima University, Japan, in 2020, this study investigated the socioeconomic and psychological profiles of victims of trending and special financial fraud such as fictitious billing fraud, loan guarantee fraud, and refund fraud. It was found that financial fraud victims' profiles are dissimilar at the aggregate and specific levels. At the specific level, victim profiles were diverse, that is, in fictitious billing fraud, loan guarantee fraud, and refund fraud cases. Males, married, and financially less satisfied people were more often victims of fictitious billing fraud; less anxious people were more likely victims of loan guarantee fraud; and older, asset-holding, and less-income-generating respondents were found to be victims of refund fraud. Our results also show some commonalities in the victims' profiles. For example, financially less-literate people were found to be more likely victims of fictitious billing fraud and loan guarantee fraud. Finally, respondents who lived with their family, those who did not have careful buying habits, and those who suffer from bouts of loneliness were found to be common victims of all types of special financial fraud. The results of our study suggest that a one-size-fits-all policy cannot effectively combat financial fraud.