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Yizhou Zhuang, Amir Erfanian, and Rong Fu

regression models. 2. Data and methodology a. Data We used the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERAI) ( Dee et al. 2011 ) for the moisture budget analysis described in section 2c . The ERAI reanalysis provides 6-hourly upper-air parameters from 1979 to near-real-time and its data are available online ( http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/ ). The atmospheric model has a hybrid sigma-pressure vertical coordinate system with 60

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Shanshui Yuan, Steven M. Quiring, and Chen Zhao

the SPI as a proxy for soil moisture and applied quantile regression to identify the relationship between the SPI and summer temperature extremes in southeastern Europe. Their results indicated that extreme heat tended to intensify over dry soils. Ford et al. (2017) also used the SPI to represent soil moisture deficits and identify the long-term variability of soil moisture–maximum temperature coupling over the continental United States. They found that the strength of land–atmosphere coupling

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Richard Seager, Jennifer Nakamura, and Mingfang Ting

drought onsets and terminations, respectively. c. Forecasts at various lead times of DO&Ts over the southern Great Plains The analysis to date has considered the general predictability of precipitation over the southern Great Plains so next we turn to forecasts of DO&Ts. It is reasonably clear that predictability drops as lead time increases. Hence, Fig. 3 presents a summary plot of prediction of DO&Ts at the seasonal time scale of lead times of 0.5–2.5 months. For the seasonal predictions, shown in

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