An increasing focus of climate change studies is the projection of storm events characterized by heavy, very heavy, extreme, and/or intense precipitation. Projected changes in the spatiotemporal distributions of such intense precipitation events remain uncertain due to large measures of variability in both the definition and evidence of increased intensity in the upper percentile range of observed daily precipitation distributions, particularly on a regional basis. As a result, projecting changes in future precipitation at the upper tail of the distribution (i.e., the heavy to heaviest events), such as through the use of stochastic weather generator programs, remains challenging. One approach to address this challenge is to better define what constitutes intense precipitation events and the degree of location-specific adjustment needed for the weather generator programs to appropriately account for potential increases in precipitation intensity due to climate change. In this study, we synthesized information on categories of intense precipitation events and assessed reported trends in the categories at national and regional scales within the context of applying this information to stochastic weather generation. Investigations of adjusting weather generation models to include long-term regional trends in intense precipitation events are limited, and modeling trends in site-specific future precipitation distributions forecasted by weather generator programs remains challenging. Probability exceedance curves and variations between simulated and observed distributions can help in modeling and assessment of trends in future extreme precipitation events that reflect changes in precipitation intensity due to climate change.
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