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Climate models robustly imply that some significant change in precipitationpatterns will occur. Models consistently project that the intensity ofindividual precipitation events increases by approximately 6-7%/K, followingthe increase in atmospheric water content, but that total precipitationincreases by a lesser amount (1-2 %/K in the global average in transient runs).Some other aspect of precipitation events must then change to compensate forthis difference. We develop here a new methodology for identifying individualrainstorms and studying their physical characteristics - including startinglocation, intensity, spatial extent, duration, and trajectory - that allowsidentifying that compensating mechanism. We apply this technique toprecipitation over the contiguous U.S. from both radar-based data products andhigh-resolution model runs simulating 80 years of business-as-usual warming. Inmodel studies, we find that the dominant compensating mechanism is a reductionof storm size. In summer, rainstorms become more intense but smaller, inwinter, rainstorm shrinkage still dominates, but storms also become lessnumerous and shorter duration. These results imply that flood impacts fromclimate change will be less severe than would be expected from changes inprecipitation intensity alone. We show also that projected changes are smallerthan model-observation biases, implying that the best means of incorporatingthem into impact assessments is via "data-driven simulations" that applymodel-projected changes to observational data. We therefore develop asimulation algorithm that statistically describes model changes inprecipitation characteristics and adjusts data accordingly, and show that,especially for summertime precipitation, it outperforms simulation approachesthat do not include spatial information.

Changes in Spatio-temporal Precipitation Patterns in Changing Climate Conditions