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Christopher A. T. Ferro and David B. Stephenson

1. Introduction Extreme weather events such as high wind speeds, heavy precipitation, or high temperatures can have severe impacts on society. Improving predictions of such events therefore has a high priority in national weather services, and an important part of this activity is to determine whether or not prediction quality is improved when prediction systems are updated. Assessing the quality of predictions of extreme weather events, however, is complicated by the fact that measures of

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Pao-Shin Chu, Xin Zhao, Ying Ruan, and Melodie Grubbs

mainland United States, extreme rainfall events in Hawaii have received little attention. Because of the socioeconomic repercussions of heavy rainfall and the associated floods on the islands, it is important to understand the frequency, intensity, locations, and patterns of these extreme events across the entire Hawaiian Islands. We thus propose to investigate the nature and spatial distributions of heavy and very heavy rainfall events in Hawaii. Three different methods have commonly been used to

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Megan C. Kirchmeier-Young, David J. Lorenz, and Daniel J. Vimont

1. Introduction While extreme events are of interest for many climate impacts studies, global coupled general circulation models (GCMs) often mute extremes with their coarse resolution, particularly for precipitation (e.g., Sillmann et al. 2013 ), and alone are not sufficient for assessments on a regional or local level. Downscaling methodologies aim to solve this scale mismatch issue by extracting high-resolution information from the coarse-resolution simulations. Downscaling methods can be

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Alice M. Grimm and Renata G. Tedeschi

of South America during the different phases of the El Niño–Southern Oscillation (e.g., Ropelewski and Halpert 1987 , 1989 ; Aceituno 1988 ; Rao and Hada 1990 ; Grimm et al. 1998 , 2000 ; Grimm 2003 , 2004 ). Given the dependence of monthly to seasonal precipitation amounts on the frequency of extreme precipitation events, it is reasonable to expect the frequency of extreme rainfall events to be modulated by ENSO in some preferred locations. However, this does not mean that regions with

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Er Lu, Wei Zhao, Xukai Zou, Dianxiu Ye, Chunyu Zhao, and Qiang Zhang

formation mechanisms of the extreme events and is particularly important to the reliable assessment of the long-term changes in the extremes. Karl et al. (1996) constructed the climate extremes index (CEI) with multiple quantities, attempting to indicate the overall extreme condition in the climate. In many previous studies, extremes were extracted from a single climate quantity (e.g., the precipitation) and for each of the observation stations or a specific region examined. With daily precipitation

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Christopher A. T. Ferro

1. Introduction Forecasting extreme weather events is a vital task and a focus for international research activities such as The Observing-System Research and Predictability Experiment (THORPEX) of the World Meteorological Organization. This article addresses a key component of this research: how should we assess the quality of forecasts of extreme events? We focus on events that are both extreme and rare. Such events pose at least three difficulties for forecast verification. First, only a

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Gérémy Panthou, Alain Mailhot, Edward Laurence, and Guillaume Talbot

). Season (see, e.g., Berg et al. 2009 ; Mishra et al. 2012 ; Shaw et al. 2011 ): For Europe, Berg et al. (2009) found a monotonic increase structure in winter, a monotonic decrease structure in summer, and a peak-like structure for spring and autumn. Time scale: Many studies have shown that the time scale over which extreme rainfall events are estimated has a huge impact on the P extr – T a relationship ( Lenderink and van Meijgaard 2008 ; Haerter et al. 2010 ; Hardwick Jones et al. 2010

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Juxiang Peng, Yuanfu Xie, and Zhaoping Kang

with cloud ice, T w is the reference temperature for a region with cloud water, T i and T w are both 0°C, T 1 and T 2 are the reference temperatures of mixed cloud ice and water, here T 1 = −38°C and T 2 = 0°C, T is the ambient temperature. Using this forward operator of cloud optical depth and cloud phase, the cloud optical depth products can be assimilated in STMAS analyses. 3. Application to an extreme precipitation event For an extreme precipitation event that lasted 8 days from

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Gregory C. Jennrich, Jason C. Furtado, Jeffrey B. Basara, and Elinor R. Martin

1. Introduction Extreme precipitation events are among the most devastating natural hazards in the contiguous United States (CONUS). These events pose significant risks and far-reaching impacts to life, property, and the economy. From 1980 to 2018, the top 30 U.S. inland flooding events cost a combined $124 billion and resulted in over 500 fatalities ( National Centers for Environmental Information 2019 ). Although flooding can arise from many sources (e.g., rapid snowpack melt, overflowing

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Megan C. Kirchmeier-Young, Francis W. Zwiers, and Nathan P. Gillett

. 2007 ). Although the rate of sea ice loss decreased subsequently, both short-period trends were within the range of natural variability plus an all-forcing (natural and anthropogenic) signal ( Swart et al. 2015 ). A new minimum was reached in 2012, with the Arctic experiencing a September average SIE of only 3.62 × 10 6 km 2 ( Fetterer et al. 2002 , a continuously updated sea ice index). To what extent can these extreme minimum events in Arctic SIE be attributed to anthropogenic influence on the

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