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Extreme precipitation can have significant adverse impacts on infrastructure and property, human health, and local economies. This paper examines recent changes in extreme precipitation in the northeast United States. Daily station data from 58 stations missing less than 5% of days for the years 1979–2014 from the U.S. Historical Climatology Network were used to analyze extreme precipitation, defined as the top 1% of days with precipitation. A statistically significant (95% confidence level) increasing trend of the threshold for the top 1% of extreme precipitation events was found (0.3 mm yr−1). This increasing trend was due to both an increase in the frequency of extreme events and the magnitude of extreme events. Rainfall events ≥ 150 mm (24-h accumulation) increased in frequency from 6 events between 1979 and 1996 to 25 events between 1997 and 2014, a 317% increase. The annual daily maximum precipitation, or the highest recorded precipitation amount in a given year, increased by an average of 1.6 mm yr−1, a total increase of 58.0 mm. Decreasing trends in extreme precipitation were observed east of Lake Erie during the warm season. Increasing trends in extreme precipitation were most robust during the fall months of September, October, and November, and particularly at locations further inland. The analysis showed that increases in events that were tropical in nature, or associated with tropical moisture, led to the observed increase in extreme precipitation during the fall months. [ABSTRACT FROM AUTHOR]

El Niño, the Indian Ocean Dipole (IOD), and the Tropical North Atlantic variability (TNA) can influence southeastern South America (SESA) rainfall, particularly during springtime. A recent study has shown that during the last century, there were two synchronization periods (1930s and 1970s) in which different tropical oceans interacted among themselves and, in turn, induced precipitation anomalies over SESA. In this study, we evaluate how this collective influence of the tropical oceans on SESA precipitation observed during the twentieth century, can change in the next century as result of anthropogenic forcing. To do so, we use the output of seven different CMIP5 models and construct a network using as nodes the indices of the Niño3.4, the TNA, the IOD and rainfall over SESA. After evaluating their skill in representing the observed network statistics during the twentieth century, we study changes in the network under RCP4.5 and 8.5 global warming scenarios in the twenty-first century. Focusing on the grand CMIP5 ensemble mean, results suggest that an anthropogenic forcing would increase the number of synchronization periods per century, their time length, and the connectivity between nodes (with the exception of TNA and IOD in RCP8.5). The stronger connectivity of SESA precipitation with the tropical oceans in both scenarios suggest an increase of the oceanic influence on rainfall over SESA as a result of anthropogenic forcing, which would enhance its seasonal predictability. However, these results have to be taken with caution because there is a large disparity in model behavior and thus a large uncertainty in conclusions suggested from the grand ensemble mean. [ABSTRACT FROM AUTHOR]

Climate models robustly imply that some significant change in precipitation patterns will occur. Models consistently project that the intensity of individual precipitation events increases by approximately 6%-7% K−1, following the increase in atmospheric water content, but that total precipitation increases by a lesser amount (1%-2% K−1 in the global average in transient runs). Some other aspect of precipitation events must then change to compensate for this difference. The authors develop a new methodology for identifying individual rainstorms and studying their physical characteristics-including starting location, intensity, spatial extent, duration, and trajectory-that allows identifying that compensating mechanism. This technique is applied to precipitation over the contiguous United States from both radar-based data products and high-resolution model runs simulating 80 years of business-as-usual warming. In the model study the dominant compensating mechanism is a reduction of storm size. In summer, rainstorms become more intense but smaller; in winter, rainstorm shrinkage still dominates, but storms also become less numerous and shorter duration. These results imply that flood impacts from climate change will be less severe than would be expected from changes in precipitation intensity alone. However, these projected changes are smaller than model-observation biases, implying that the best means of incorporating them into impact assessments is via 'data-driven simulations' that apply model-projected changes to observational data. The authors therefore develop a simulation algorithm that statistically describes model changes in precipitation characteristics and adjusts data accordingly, and they show that, especially for summertime precipitation, it outperforms simulation approaches that do not include spatial information. [ABSTRACT FROM AUTHOR]