Changes in extreme precipitation are associated with changes in their probability distributions and the characteristics of quantiles derived from fitted distributions. In this study, the linear quantile regression method is employed to analyse spatio‐temporal trends of extreme precipitation and to study the impact of North Atlantic tropical cyclones in the distribution of extreme precipitation for the southeastern United States. Daily annual maximum precipitation over the period of 64 years (1950–2013) for 107 sites was used for the analysis. Our results show that changes in upper quantiles of the distributions of the extreme precipitation have occurred in the southeastern United States. Analysis of the potential changes in the distribution of the extreme precipitation by separating the historical record into two periods, that is, before and after 1981, reveals that upper‐quantile trends have increasing magnitude in most of the sites for the latest time period. Analysis of the impact of tropical cyclones in the extreme precipitation distribution shows that overall the heavy rainfall events in the recent decades may have been caused by tropical cyclones. Such results are particularly useful for water managers who are more concerned with extreme values rather than the averaged one. Hence, our study has significant implication in environmental and infrastructural assessment as well as disaster risk management.

Global warming is likely to provoke extreme storms in the eastern United States (eUS), ultimately affecting the probabilistic distribution of the dates of daily maximum precipitation. In this study, probabilistic properties of timing of annual maximum precipitation (AMP) were studied using circular statistics at 583 sites in the eUS (1950–2019). A kernel circular density method was applied to examine distributional modes of timing of AMP. The results of circular median show that seasonality is pronounced across the eUS with many locations having their median date of occurrence in summer, and AMP seasonality is strong in the East North Central region. Similarly, results of circular density method applied to the distribution of AMP timing shows that around 90% of the sites have two or three modes of AMP seasonality in the eUS. Comparison of seasonality between two historical records of equal length (1950–1984 and 1985–2019) shows great spatial variability across the eUS. Temporal changes in seasonal modes for AMP dates revealed four different cases of seasonality changes: (i) weakening of seasonality, (ii) strengthening of seasonality, (iii) strong seasonality for both the old and recent periods, (iv) or uniform or no preferred seasonality for both periods. While a spatial coherence of seasonality changes was not observed, majority of sites showed strong seasonality (case iii) for old and recent periods mainly during summer and fall seasons.

Background: Seasonal influenza (SI) is an acute respiratory illness that exerts a severe impact on human life year-round. Yet, very few studies have been conducted to investigate its peak timing for different age groups. Objective: To evaluate the average peak calendar time and intensity for the incidence of SI for different age groups. Methods: The study uses laboratory-confirmed Influenza data from the Centers for Disease Control and Prevention (CDC) of the USA with age groups 2, 11, 34, 57 and 65 years during 2009–2018 for the analysis. A non-parametric method of estimation of a circular probability distribution called likelihood cross-validation method has been utilised. Results: The average peak date of incidence for age groups 2 and 11 is around the last week of December. However, the date shifts to the last week of January to the first week of February for other groups. Age groups 65 and 2 years experienced the most severe impact among all. Discussion: The average peak time for SI incidence is between the last week of December to January with a single peak time for every age group. However, the incidence seems to develop an additional moderate peak time for age group 65.

Bhikhari Tharu, Keshav Pokhrel, Gokarna Aryal, Ram C. Kafle, Netra Khanal. CSAM 2021;28:119-34. https://doi.org/10.29220/CSAM.2021.28.2.119