Changing seasonality of daily and monthly precipitation extremes for the contiguous USA and possible connections with large‐scale climate patterns
Changing seasonality of daily and monthly precipitation extremes for the contiguous USA and possible connections with large‐scale climate patterns
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DOI:
10.1002/joc.7994
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发表时间:
2023-01
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影响因子:
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通讯作者:
N. Dhakal;Bhikhari Tharu;Ali Aljoda
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文献类型:
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作者:
N. Dhakal;Bhikhari Tharu;Ali Aljoda
Temporal changes in the seasonality of extreme precipitation, and possible teleconnections between the seasonality of extreme precipitation and large‐scale climate patterns are not well understood. In this study, we investigated temporal changes in seasonality of annual daily maximum (ADM) and monthly maximum (MM) precipitation indices over the period 1951–2014 for 1,108 stations across the contiguous USA. We also examined seasonality of extreme precipitation during negative and positive phases of three major oscillations: the El Niño–Southern Oscillation, the Northern Atlantic Oscillation, and the Pacific Decadal Oscillation. Our results show that many climate regions within the contiguous USA display distinct seasonality for both ADM and MM. Comparison of seasonality between two historical records of equal length, that is, before and after 1981, shows great spatial variability across the contiguous USA. While a spatial coherence of change in the mean date of occurrence of extreme precipitation across a large area is not visible, a cluster of stations showing decrease in strength of seasonality for the recent period is concentrated in the eastern Gulf Coast and coastal sites of Northeast and Northwest regions. Extreme precipitation seasonality during negative and positive phases of three climate indices revealed that large‐scale climate variabilities have a strong influence on the mean date of occurrence of extreme precipitation but generally weak influence on the strength of seasonality in the contiguous USA. Results from our study might be helpful for sustainable water resource management, flood risk mitigation, and prediction of future precipitation seasonality.