**1. Introduction**

Extreme weather events can change local climate characteristics such as the mean values of temperature and precipitation, as well as their variabilities. As one of the most important climate variables, precipitation, has significant impacts on the hydrological processes and water resources management of a river basin [1]. In the context of climate change, the frequency of extreme weather events has increased, causing

more serious natural water-related disasters such as floods and droughts [2–5]. The increase in extreme rainfall always leads to floods, which have remarkable impacts on the local ecology, industry, and social economy. According to the statistics, more than 60% of floods are caused by extreme rainfall since the twentieth century [6, 7]. Therefore, more attention has been paid to studying the importance of extreme rainfall. However, extreme rainfall in different regions can vary greatly in spatial distribution, scope, frequency, duration, and severity [8, 9]. Moreover, the responses to extreme rainfall can vary greatly in different regions. Thus, it is of great importance to investigate the characteristics of extreme rainfall in a designated region.

The traditional hydrological frequency analysis of extreme rainfall and flooding is mainly based on stationary assumptions. However, several studies have shown that extreme rainfall is increasing in many parts of the world due to varying degrees of instability. Li et al. believed that the non-stationarity of the rainfall series could play an important role in the prediction and risk analysis of extreme rainfall [10]. Liu et al. observed that the non-stationarity of extreme rainfall in the Weihe River basin was sensitive to environmental changes [11]. Chen et al. studied the non-stationarity of the maximum daily rainfall in Taiwan and found that the high uncertainty of the nonstationarity of the maximum daily rainfall would lead to a difference in the predicted return period [12]. Beguería et al. established the General Pareto Distribution (GPD) model and found that extreme rainfall in Northwest Spain decreased significantly in winter but increased significantly in summer [13]. Sugahara et al. believed that, under non-stationary conditions, extreme rainfall in Sao Paulo, Brazil, showed a clear increasing trend [14]. Lee et al. found that the peak over threshold (POT)-GPD combined model would be more suitable for predicting future rainfall under nonstationary conditions [15]. Syafrina et al. proposed that the non-stationary extreme rainfall series is more suitable for Generalized Extreme Value Models (GEV) in Sabah [16]. Therefore, it has important significance to study the non-stationarity of extreme rainfall.

Currently, there is no clear definition of extreme rainfall. According to the World Meteorological Organization (WMO), most studies have used percentile rainfall or fixed rainfall as thresholds, such as 95% or 50 mm rainfall [17–20]. However, in a nonstationary state, the difference in the choice of the threshold will generate a large amount of uncertainty, even based on stationary assumptions. Different threshold selections will also have impacts on modeling [7, 21]. Vu and Mishra demonstrated that the models and parameters selected under different thresholds would affect the non-stationarity of extreme rainfall, and a suitable extreme rainfall sequence needed to be selected [22]. Sugahara et al. studied the distribution of extreme rainfall under different thresholds in Sao Paulo and believed that 98% of the daily rainfall value was the most suitable for the extreme rainfall threshold in that region [14]. Liu et al. studied the extreme rainfall in the Weihe River basin and believed that 95% rainfall was suitable [11]. Therefore, it is necessary to study the non-stationarity of extreme rainfall under different thresholds and detect the specific extreme rainfall threshold in a designated region.

Among all, the climatic and non-climatic factors, large-scale climatic patterns, for example, El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Western Pacific Subtropical High (WPSH), and Arctic Oscillation (AO), are regarded as the most important factors affecting rainfall [23–27]. Villarini and Denniston showed that ENSO had a significant control effect on extreme rainfall in Australia [28]. Limsakul and Singhruck claimed that PDO was one of the most important factors affecting extreme rainfall changes in Thailand [29]. Fu et al. found that ENSO could

*Threshold Recognition Based on Non-Stationarity of Extreme Rainfall in the Middle… DOI: http://dx.doi.org/10.5772/intechopen.109866*

affect China's extreme rainfall trends and changes [30]. Zhang and Liu et al. demonstrated that WPSH was one of the main driving factors of the summer extreme rainfall in China [31, 32]. However, previous studies exploring the teleconnections between extreme rainfall and large-scale climatic patterns were usually based on trend analysis and correlation coefficients [33], which could not fully reveal their correlations.

The main purposes of this study are: (1) to divide the middle and lower reaches of the Yangtze River basin (MLRYRB) into sub-regions to perform the non-stationary detection of extreme rainfall, and to identify the non-stationarity of extreme rainfall with different thresholds; (2) to screen out the most suitable threshold range of extreme rainfall in each sub-region based on distribution fitting of extreme rainfall; and (3) to explore the teleconnections between extreme rainfall and large-scale climatic patterns. The main significance of this study is to explore the non-stationary pattern of extreme rainfall in the MLRYRB when the threshold changes. Combined with the nonstationarity of extreme rainfall in different sub-regions, the extreme rainfall threshold range in the region is accurately screened. In addition, the cross-wavelet analysis method will be used to comprehensively explore the relationships between ENSO, WPSH, and extreme rainfall in a different time and frequency domains.
