The aim of this paper is to present a statistical downscaling method in which the relationships between present-day daily weather patterns and local rainfall data are derived and used to project future shifts in the frequency of heavy rainfall events under changing global climate conditions. National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP/NCAR) reanalysis data from wet season months (November to April) 1958–2010 are composited for heavy rain days at 12 rainfall stations in the Hawaiian Islands. The occurrence of heavy rain events (days with amounts above the 90th percentile estimated from all wet season rain days 1958–2010) was found to be strongly correlated with upper level cyclonic circulation anomalies centered northwest of Hawai‘i and south-to-north transport of water vapor in the middle troposphere. The statistical downscaling model (SD) developed in this study was able to reproduce the observed interannual variations in the number of heavy rain events based on cross-validation resampling during the more recent interval 1978–2010. However, multidecadal changes associated with the mid-1970s' climate shift were not well reproduced by the SD using NCEP/NCAR reanalysis data, likely due to inhomogenities in the presatellite period of the NCEP/NCAR reanalysis. Application of the SD to two model scenarios from the CMIP3 database indicates a reduction of heavy rain events in the mid- to late 21st century. Based on these models, the likelihood of a widespread increase in synoptic heavy rain events in Hawai‘i as a result of anthropogenic climate change is low over the remainder of the century.
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