Impact of ENSO Events on Droughts in China

Abstract

The El Niño Southe58rn Oscillation (ENSO) is a typical oscillation affecting climate change, and its stable periodicity, long-lasting effect, and predictable characteristics have become important indicators for regional climate prediction. In this study, we analyze the Standardized Precipitation Evapotranspiration Index (SPEI), the Niño3.4 index, the Southern Oscillation Index (SOI), and the Multivariate ENSO Index (MEI). Additionally, we explore the spatial and temporal distribution of the correlation coefficients between ENSO and SPEI and the time lag between ENSO events of varying intensities and droughts. The results reveal that the use of Nino3.4, MEI, and SOI produces differences in the occurrence time, end time, and intensity of ENSO events. Nino3.4 and MEI produce similar results for identifying ENSO events, and the Nino3.4 index accurately identifies and describes ENSO events with higher reliability. In China, the drought-sensitive areas vulnerable to ENSO events include southern China, the Jiangnan region, the middle and lower reaches of the Yangtze River, and the arid and semi-arid areas of northwestern China. Droughts in these areas correlate significantly with meteorological drought, and time-series correlations between ENSO events and droughts are significantly stronger in regions close to the ocean. Drought occurrence lags ENSO events: when using the Niño3.4 index to identify ENSO, droughts lag the strongest and weakest El Niño events by 0–12 months. However, when using the MEI as a criterion for ENSO, droughts lag the strongest and weakest El Niño events by 0–7 months. The time lag between the strongest ENSO event and drought is shorter than that for the weakest ENSO event, and droughts have a wider impact. The results of this study can provide a climate-change-compatible basis for drought monitoring and prediction.

1. Introduction

Drought is a globally widespread climatic phenomenon with broad spatial coverage and long duration and is one of the most common and widespread natural disasters worldwide [1]. Due to global warming, the frequency and intensity of extreme meteorological hazards such as droughts will increase [2,3]. Compared with natural disasters such as floods and typhoons, droughts are very difficult to identify, monitor, and analyze because of problems associated with objectively quantifying their characteristics in terms of intensity, magnitude, duration, and spatial extent. Moreover, drought is a multiscale phenomenon, which adds significant complexity to determining its onset, extent, and cessation [4,5]. Therefore, exploring the mechanisms that cause drought and accurately predicting the occurrence of droughts are important for drought defense, risk prevention and control, and reducing drought losses.

Droughts are extreme meteorological and hydrological events having severe impacts on the natural environment and socioeconomic conditions of the affected region. Nowadays, the identification of drought is based on drought indices allowing, on the one hand, the determination of the threshold indicating drought at different time scales. On the other hand, the classification of conditions is according to their severity and location. These drought indicators need to take into account various hydrological and climatic variables such as precipitation, soil moisture content, runoff, groundwater, vegetation evapotranspiration, and snow cover. Fortunately enough, the widely used World Meteorological Organisation-recommended Standardized Precipitation Index (SPI), which is based on precipitation only as a drought index, has been modified to the Standardized Precipitation Evapotranspiration Index (SPEI). This extended SPI version is designed to take into account both precipitation (P) and potential evapotranspiration (PET) in determining the drought characteristics of a region [5,6,7]. When the SPEI value is less than 0.5, it indicates the occurrence of drought. Thus, the SPEI captures the main impact of increased temperatures on water demand. In this way, it not only identifies the expected impact on precipitation but also incorporates the predicted severity of the drought due to temperature variability. Therefore, the index is more suited to detecting, monitoring, and exploring the impacts of global warming on drought conditions [8].

The El Niño Southern Oscillation (ENSO) consists of periodic fluctuations (i.e., every 2–7 years) in sea surface temperature (El Niño) and air pressure in the upper atmosphere (Southern Oscillation) across the equatorial Pacific Ocean and significantly affects climate patterns in all regions of the world. ENSO has become an important indicator for regional climate prediction because of its stable periodicity, persistent signal, and predictability [6]. ENSO events significantly impact meteorological elements such as precipitation and temperature on a global scale, which in turn can lead to extreme weather events such as droughts [7,8]. Many studies have shown that the frequency of global drought disasters after El Niño events is twice that of normal years, and drought prediction based on ENSO events is more accurate than in other years [9]. Drought prediction using ENSO has become one of the hot spots in drought research and has been used in countries such as the United States [10], China [11], Africa [12], Chile [13], Australia [14], Vietnam [15], and India [3]. It has also been used in large basins of the world, such as the Lancang-Mekong River basin [16], the Nile basin [17], the Yangtze River basin [18], and the Columbia River basin [19].

The effects of ENSO on precipitation and temperature are not synchronous, so a time lag exists between ENSO events and droughts [20]. Chiew et al. investigated the teleconnection between ENSO and climate factors and revealed a time lag of 3–5 months between ENSO events and variations in precipitation and runoff and that meteorological drought and hydrological drought lag ENSO events by 3 months or more [21]. Chiew et al. analyzed the correlation between ENSO and drought indexes and reported a lag of over three months between ENSO events and meteorological drought and over six months with hydrological drought in eastern Australia [21]. Dewi et al. studied the spatial and temporal relationship between ENSO and rainfall variations and rice yields in Indonesia, showing that, after an El Niño event, precipitation decreases, leading to a decrease in rice yield. They also reported that a lagged correlation exists between ENSO events and agricultural drought, with the lag being longer than for a meteorological drought [22]. Özger et al. studied the lagged correlation in Texas between ENSO and drought and found lags of 15–19 months on the interdecadal scale. On the interannual scale, ENSO has a lagged correlation of 6–13 months with drought [23]. Zambrano Mera et al. made drought predictions for the Manabi River basin based on the time lag between ENSO and drought, revealing a 7–9 months lag between ENSO and drought for this basin [24]. The time difference between ENSO events and drought can thus be used for drought prevention and control, which is important for reducing drought-induced losses [25].

ENSO events of different intensities have different effects on global temperature, precipitation, and evaporation and result in different frequencies, intensities, durations, and impacts of droughts [26]. An ENSO event may be classified as strong or weak in terms of duration, intensity, and coverage [27]. Lyon shows that the likelihood and severity of drought during a strong El Niño event is 50–70% greater than in a normal year, and the area affected by an El Niño event is two to three times greater than that of a weak El Niño event or a normal year [28]. The El Niños in 1982–1983, 1997–1998, and 2015–2016 were the strongest on record and lasted longer than the others, with catastrophic weather around the world associated with these severe El Niños [29]. Due to the strong El Niño in 2015, global temperature increased by 0.9 °C, the hottest year since the 20th century [30]. In early 2015 alone, about 36.9% of the United States was in drought [31]. During the 2015–2016 El Niño, more than 70% of Australia experienced significant decreases in precipitation, and Africa experienced extreme drought droughts lasting 3–9 months, triggering large-scale protracted agricultural droughts in East and South Africa [32]. The strong El Niño of 2015—2016 led to less precipitation in western Xinjiang, northwestern Tibet, central eastern Inner Mongolia, northern China, and the Yellow River and Huaihe River basins and to a significant increase in the frequency of meteorological droughts and hydrological droughts in the Yangtze River and Pearl River basins [33]. The drought in the Iberian Peninsula, France, southern Benelux, and central Germany developed rapidly in May 2015 due to the strong El Niño, and in August, the drought extended to eastern Europe [30]. The strong El Niño event in 2015–2016 resulted in moderate or severe drought over more than 30% of the Amazon rainforest and extreme drought over more than 13% of the forest, with five times more drought area than in normal or weak ENSO years [34].

El Niño (La Niña) is associated with a band of warm (cold) water, occurring every few years over the central to eastern equatorial Pacific [35]. On the global scale, in an El Niño year, the temperature is higher, and the continental precipitation decreases; in a La Niña year, the temperature is lower, and the average continental precipitation increases. However, China is a sensitive area to climate change because of its vast territory, complex and diverse natural conditions, and the many factors that affect China’s climate [36]. The occurrence and development of ENSO events are important factors affecting the climate of China [37]. The spatial and temporal patterns of precipitation in China are modified by ENSO events [38]. When El Niño occurs, precipitation in the Jianghuai River basin will be high and flooding, whereas precipitation in the Yellow River basin and northern China tends to be low, with the area being prone to drought. On the contrary, in La Niña years, the temperature in winter and spring is low, and the temperature in summer and autumn is high in Northwest China due to the concentration of heavy rainfall in China. La Niña reduces precipitation in the Huaihe River basin in China, so droughts may occur, whereas precipitation in the Yellow River basin, northern China, and the southern Yangtze River basin, and southern China is likely to increase [39,40].

Numerous studies have explored the time-lag correlation between ENSO events and drought in China by establishing a correlation between ENSO and drought or climate factors for a particular province or watershed. For example, Li et al. found a 1–2-month time-lag teleconnection between ENSO and meteorological drought in Taiwan [11]. Sun et al. revealed how ENSO affects meteorological drought and agricultural drought in Anhui Province, China, and showed that ENSO leads to a 68% probability of drought occurrence during the rainy season in Anhui Province, with a 3-month time lag between El Niño and meteorological drought and a 12-month time lag with agricultural drought in Anhui Province [41]. Liu et al. showed that the Poyang Lake basin interannual and interdecadal drought has cycles of 3–6 and 15–30 years, with a 1–2-month time lag between El Niño and drought [42]. The time lag between El Niño and drought in the Yangtze and Pearl River basins is 5–6 months [18].

However, these studies neglect differences in time lag between ENSO events of different intensities and droughts and also how different intensities of ENSO events affect the extent of droughts. Therefore, to improve the accuracy of drought prediction, it is important to study the time lag between ENSO events of different intensities and drought in China, and especially how the extent of drought depends on the strength of ENSO events.

This work uses various ENSO indexes and the standardized precipitation evapotranspiration index (SPEI) to analyze the correlations between ENSO events of different intensities and drought indexes from the perspective of meteorological drought. In particular, we focus on (1) clarifying how the ENSO strength affects drought in China through a Pearson correlation analysis and (2) studying and quantifying the time-lag correlation between drought and ENSO events through a time series analysis. This study explores how ENSO affects the range of drought, thus providing a basis for accurate drought monitoring and prediction and for further improving drought management. The data and methods are presented in Section 2, the main results are presented in Section 3, and the discussion and conclusion are presented in Section 4 and Section 5, respectively.

2. Materials and Methods

2.1. Meteorological Data

Meteorological data were obtained from the China Meteorological Data Network (http://data.cma.cn/; accessed on 11 January 2021). They include temperature, relative humidity, wind speed, sunshine data, precipitation, and evaporation, which were taken from the China Surface Climate Data Diurnal Dataset (V3.0) produced by the National Meteorological Information Center and have undergone quality control. Meteorological data from 577 meteorological stations that met the conditions were selected, avoiding several meteorological stations with missing data, such as in Tibet, Guizhou, Jiangxi, Zhejiang, the south-central part of Hebei, and the western part of Inner Mongolia. We used the climate zoning data from the Resource and Environment Science and Data Center of the Chinese Academy of Sciences to divide different climatic zones in China (https://www.resdc.cn/ (accessed on 11 January 2021)). The eco-geographical regions are shown in

Table 1. Codes of eco-geographical regions.

Temperature Zone	Dry–Wet Partition	Natural Region
I Cool Temperate Zone	A Humid Region	IA1 Daxing’an Mountains
II Mid-Temperate Zone	A Humid Region	IIA1 Sanjiang Plain
		IIA2 East Upland Area of Northeast China
		IIA3 Front Mountain Plain of Eastern Northeast China
	B Semi-Humid Region	IIB2 Southern Daxing’an Mountains
		IIB3 Plain and Hills Sanhe Piedmont
	C Semi-Arid Region	IIC1 Southwestern Songliao Plain
		IIC2 Northern Daxing’an Mountains
		IIC3 Eastern Inner Mongolia Plateau
	D Arid Region IID3 Junggar Basin	IID1 Western Inner Mongolia Plateau and Hetao
		IID2 Alxa and Hexi Corridor
		IID4 Altai Mountain and Tacheng Basin
		IID5 Ili River Basin
III Warm Temperate Zone	A Humid Region	IIA1 Jiaodong Mountain Hills in Eastern Liaoning Province
	B Semi-Humid Region	IIIB1 Mountain and Hills in Central Shandong
		IIIB2 North China Plain
		IIIB3 Mountain and Hills in North China
		IIIB4 Guanzhong Basin in South Shanxi
	C Semi-Arid Region	IIC1 Hilly and Plateau in Central Shanxi, Northern Shanxi, and Eastern Gansu
	D Arid Region	IIID1 Tarim and Turpan Basins
IV Northern Subtropical Zone	A Humid Region	IVA1 South of the Huaihe River and Middle and Lower Reaches of the Yangtze River
		IVA2 Hanzhong Basin
V Middle Subtropical Zone	A Humid Region	VA1 Jiangnan Hills
		VA2 Jiangnan and Nanling Mountains
		VA3 Guizhou Plateau
		VA4 Sichuan Basin
		VA5 Yunnan Plateau
		VA6 South Limb of Eastern Himalayan
VI Southern Subtropical Zone	A Humid Region	VIA1 Mountain Plain in Central and Northern Taiwan
		VIA2 Hilly Plain of Fujian, Guangdong, and Guangxi
		VIA3 Mountain Hills in Central Yunnan
VII Edge Tropical Zone	A Humid Region	VIIA1 Lowlands in Southern Taiwan
		VIIA2 Mountain Hills in Qionglei
		VIIA3 Valley Hills in South Yunnan
VIII Central Tropical Zone	A Humid Region	VIIIA1 Qionglei Lowland and Dongsha, Zhongsha, and Xisha Islands
IX Equatorial Tropical Zone	A Humid Region	IXA1 Nansha Islands
HI Highland Subduction Zone	B Semi-Humid Region	HIB1 Hilly Plateau in Guoluo and Naqu
	C Semi-Arid Region	HIC1 Wide Valley of the South Qinghai Plateau
		HIC2 Qiangtang Plateau Lake Basin
	D Arid Region	HID1 Plateau of Kunlun
HII Highland Temperate Zone	A/B Humid Region/Semi-Humid Region	HIIA/B1 High Mountains and Canyon in Eastern Sichuan and Tibet
	C Semi-Arid Region	HIIC1 Eastern Qilian Mountains
		HIIC2 Mountain South Tibet
	D Arid Region	HIID1 Qaidam Basin
		HIID2 North Limb of Kunlun Mountain
		HIID3 Ali Mountain

.

2.2. ENSO Index

2.2.1. Southern Oscillation Index

The Southern Oscillation Index (SOI) is the difference in surface pressure over Tahiti and Darwin. The SOI is usually measured on a monthly time scale because values on shorter time scales can be affected by short-term weather fluctuations. The SOI is a good indicator of the strength of ENSO phenomena over the Pacific Ocean. SOI data for the period 1980–2018 are derived from the National Oceanic and Atmospheric Administration.

2.2.2. Niño3.4 Sea Surface Temperature Index

Based on the tropical Pacific sea surface temperature (SST), the central and eastern equatorial Pacific Ocean regions are usually divided into four ENSO-monitoring regions: Niño1 (5°–10° S, 90°–80° W), Niño2 (0°–5° S, 90°–80° W), Niño3 (5° S–5° N, 150°–90° W), and Niño4 (5° S–5° N, 160° E–150° W). Trenberth (1997) reported that small temperature changes in Niño3.4 (5° S–5° N, 170°–120° W) between Niño3 and Niño4 have a large impact on climate and can be used to accurately monitor and reflect the El Niño signal. Niño3.4 data for the period 2018 are available from the National Oceanic and Atmospheric Administration.

2.2.3. Multivariate ENSO Index

The multivariate ENSO Index (MEI) is derived from six tropical ocean observation elements by principal component analysis. The MEI is an indicator that combines sea level pressure, zonal and meridional components of the surface wind, SST, surface air temperature, and the total cloudiness fraction of the sky. The current multivariate ENSO indicator is just statistical data resulting from a principal component analysis of the six meteorological elements over the ocean. It expresses the combined condition of the ocean elements for each month and fully describes the ENSO phenomenon (Van Viet, 2021). MEI data for the period 1980–2018 were obtained from the National Oceanic and Atmospheric Administration. According to the delineation criteria for the ENSO index from the National Climate Center/China Meteorological Administration (NCC/CMA), we identified the occurrence time, end time, and intensity of the EI Nino and LaNina events for the three ENSO indices of Niño3.4, MEI and SOI respectively

Table 2. Criteria for defining ENSO years based on three indexes [Source: NCC/CMA].

ENSO Index	Criterion
Niño3.4 index	El Niño (La Niña): Maximum (Minimum) SST > 1 (<−1) standard deviation and SST > 0.5 °C (<0.5 °C) for at least 8 months
Southern Oscillation Index (SOI)	El Niño (La Niña): five-month running mean of SOI < −0.5 (>0.5) for 5 or more consecutive months between April of the year to March of the following year (+)
Multivariate ENSO Index (MEl)	El Niño (La Niña): five-month running mean of MEI > 0.5 (<0.5) for five or more consecutive months between April to March of the following (+) and the peak MEI > 1 (<1)

lists the three most widely used ENSO indicators and the criteria used to define ENSO events.

2.3. Method

2.3.1. Standardized Precipitation Evapotranspiration Index

The SPEI is mainly used to express drought severity in terms of the average deviation between precipitation and evapotranspiration. The SPEI has multiple time scales. In this study, we use the one-month scale to calculate the SPEI. To generate the SPEI, the potential evapotranspiration is calculated by using the Thornthwaite method and the difference ${\mathrm{D}}_{\mathrm{i}}$ between precipitation and evapotranspiration at various time scales is estimated according to the potential evapotranspiration. Next, the cumulative probability function density of the LOG distribution $\mathrm{P}$ is calculated. Finally, the data sequence is normalized to calculate the SPEI corresponding to each value of $\mathrm{P}$. The magnitude of the SPEI is defined as:

Calculating the monthly potential evapotranspiration (PET) by using the Thornthwaite method:

$$\mathrm{PET}=\left\{\begin{array}{c}0,\mathrm{T}<0\\ 16\left(\frac{\mathrm{N}}{12}\right)\left(\frac{\mathrm{NDM}}{30}\right){\left(\frac{10\mathrm{T}}{\mathrm{I}}\right)}^{\mathrm{M}},0\le \mathrm{T}<26.5\\ -425-32.24\mathrm{T}-0.43{\mathrm{T}}^2,\mathrm{T}>26.5\end{array}\right.$$

(1)

where $\mathrm{N}$ is the maximum number of sun hours, $\mathrm{NDM}$ is the number of days in the month, $\mathrm{T}$ is mean air temperature (°C), and $\mathrm{I}$ is heat index, which is calculated as the sum of 12 monthly index values:

$$\mathrm{I}={\displaystyle \sum }_{\mathrm{i}=1}^{12}{\left(\frac{\mathrm{T}}{5}\right)}^{1.514},\mathrm{T}>0$$

(2)

and $\mathrm{m}$ is a coefficient that depends on $\mathrm{I}$:

$$\mathrm{m}=6.75\times {10}^{-7}{\mathrm{I}}^3-7.71\times {10}^{-5}{\mathrm{I}}^2+1.79\times {10}^{-2}\mathrm{I}+0.492$$

(3)

$${\mathrm{D}}_{\mathrm{I}}={\mathrm{P}}_{\mathrm{i}}-{\mathrm{PET}}_{\mathrm{i}}$$

(4)

The calculated ${\mathrm{D}}_{\mathrm{i}}$ values are aggregated into different time series, following the same procedure as for SPI. The difference ${\mathrm{D}}_{\mathrm{n}}^{\mathrm{k}}$ in a given time n depends on the chosen time scale k (months).

$${\mathrm{D}}_{\mathrm{n}}^{\mathrm{k}}={\displaystyle \sum }_{1=0}^{\mathrm{k}-1}\left(p_{\mathrm{n}-1}-{\mathrm{PET}}_{\mathrm{N}-\mathrm{i}}\right),\mathrm{n}\ge \mathrm{k}$$

(5)

Next, the water balance is normalized into a log-logistic probability distribution to obtain the SPEI index series. The probability density function of a three-parameter log logistic distributed variable can be expressed as follows:

$$\mathrm{f}\left(\mathrm{x}\right)=\frac{\mathsf{\beta }}{\mathsf{\alpha }}{\left(\frac{\mathrm{x}-\mathsf{\gamma }}{\mathsf{\alpha }}\right)}^{\mathsf{\beta }-1}{\left[1+{\left(\frac{\mathrm{x}-\mathsf{\gamma }}{\mathsf{\alpha }}\right)}^{\mathsf{\beta }}\right]}^{-2}$$

(6)

where $\mathsf{\alpha }, \mathsf{\beta },$ and $\mathsf{\gamma }$ denote the scale, shape, and origin parameters for $\mathrm{D}$ values in the range $(\mathsf{\gamma } > \mathrm{D} <\infty )$, respectively. The calculation process of each parameter is as follows:

$$\mathsf{\beta }=\frac{2{\mathsf{\omega }}_1-{\mathsf{\omega }}_0}{6{\mathsf{\omega }}_1-{\mathsf{\omega }}_0-6{\mathsf{\omega }}_2}$$

(7)

$$\mathsf{\alpha }=\frac{\left({\mathsf{\omega }}_0-2{\mathsf{\omega }}_1\right)\mathsf{\beta }}{\mathrm{\Gamma }\left(1+1/\mathsf{\beta }\right)\mathrm{\Gamma }\left(1-1/\mathsf{\beta }\right)}$$

(8)

$$\mathsf{\gamma }={\mathsf{\omega }}_0-\mathsf{\alpha }\mathrm{\Gamma }\left(1+1/\mathsf{\beta }\right)\mathrm{\Gamma }\left(1-1/\mathsf{\beta }\right)$$

(9)

where $\mathrm{\Gamma }\left(1+1/\mathsf{\beta }\right)$ is the gamma function of $\left(1+1/\mathsf{\beta }\right)$, and ${\mathsf{\omega }}_0$ is the probability-weighted moments (PWMs) of order s for $\mathrm{D}$ data series:

$${\mathsf{\omega }}_{\mathrm{s}}=\frac{1}{\mathrm{n}}{\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{n}}{\left(1-\frac{\mathrm{j}-0.35}{\mathrm{n}}\right)}^{\mathrm{s}}{\mathrm{D}}_{\mathrm{i}}$$

(10)

where n is the number of data points, and $\mathrm{j}$ is the range of observations in increasing order. Thus, the probability distribution function of the $\mathrm{D}$ series is given by:

$$\mathrm{F}\left(\mathrm{D}\right)={\left[1+{\left(\frac{\mathsf{\alpha }}{\mathrm{D}-\mathsf{\gamma }}\right)}^{\mathsf{\beta }}\right]}^{-1}$$

(11)

Ultimately, the $\mathrm{F}\left(\mathrm{x}\right)$ value is transformed to a normal variable by means of the following approximation, and the SPEI can be calculated.

$$p=1-\mathrm{F}\left(\mathrm{x}\right)$$

(12)

$$\mathrm{W}=\left\{\begin{array}{c}\sqrt{-2\ln \left(p\right)},p\le 0.5\\ \sqrt{-2\ln \left(1-p\right)},p>0.5\end{array}\right.$$

(13)

$$\mathrm{SPEI}=\mathrm{S}\times \left(\mathrm{W}-\frac{{\mathrm{c}}_0+{\mathrm{c}}_1\mathrm{w}+{\mathrm{c}}_2\mathrm{w}}{1+{\mathrm{d}}_1\mathrm{w}+{\mathrm{d}}_1\mathrm{w}+{\mathrm{d}}_2{\mathrm{w}}^2+{\mathrm{d}}_3{\mathrm{w}}^3}\right)$$

(14)

where $\mathrm{S}=\pm 1$, $\mathrm{W}=\sqrt{-2\ln p}$ and $p$ is the distribution probability calculated before. When $p$ ≤ 0.5, p = F(x), S = +1, when p > 0.5, p = 1 − F(x), S = −1. In Equation (1), c₀, c₁, c₂, d₁, d₂ and d₃ are constants.

2.3.2. ENSO Duration and Strength

The start (end) month is defined as the earliest (latest) month for which the ENSO index meets the ENSO criterion. The duration of ENSO is the number of months from the start month to the end month, and the ENSO strength is defined by the maximum or peak index value of the event. An ENSO event is weak if the absolute value of the peak index of the event is between 0.5 and 1.3 °C, a medium event has a peak index between 1.3 and 2.0 °C, a strong event has a peak index between 2.0 and 2.5 °C, and a very strong event has a peak index greater than 2.5 °C. Note that the criteria of 1.3, 2.0, and 2.5 °C are approximately 1.5, 2.5, and 3 times the standard deviation of the Niño3.4 index, respectively [37].

2.3.3. Quantifying the Teleconnection Relationship between Different ENSO Indexes and SPEI

The telecorrelation of ENSO can be used to monitor the development of ENSO in response to drought, and the quantitative response relationship can depend on the correlation between ENSO indicators and drought indexes [43]. Niño3.4, SOI, and MEI are commonly used indicators to identify ENSO and offer different identification standards (Table 1). SPEI can accurately characterize regional wet-dry fluctuations [44]. Therefore, we use Niño3.4, SOI, and MEI for each month from 1980 to 2018 and calculate the SPEI of each month from 1980 to 2018 for 577 meteorological stations in China. The Pearson correlation analysis method is used to quantitatively analyze each month from 1980 to 2018. The correlation between Niño3.4, SOI, MEI, and SPEI of each station and the correlation coefficient and significance of different ENSO indexes and SPEI are then counted to quantify the teleconnection relationship between different ENSO indexes and SPEI, and the results are spatially visualized and analyzed.

2.3.4. Spatial and Temporal Analysis of the Time-Lag Response of Drought to ENSO Events of Different Intensities

Since the different ENSO indexes have different criteria for defining the ENSO events, the ENSO events identified using these different indexes can also differ. First, according to the results in quantifying the teleconnection relationship between different ENSO indexes and SPEI, according to the response degree of drought in China to Niño3.4, MEI, and SOI, we choose the most suitable index among Niño3.4, MEI, and SOI for identifying ENSO events. Second, we use the most suitable index for identifying ENSO (Table 2) to find the occurrence, end time, duration, and strength of ENSO events from 1980 to 2018. Third, according to the magnitude of the most suitable index for identifying ENSO events, we select the strongest and weakest ENSO events. Fourth, we determine the time-lag correlation between the strongest (weakest) ENSO event and SPEI from three aspects: (1) According to the correlation coefficient and significance, determine whether the positive and negative correlations are significant, the correlation coefficient of positive correlation is 0.075–0.18, 0.025–0.075, 0–0.025, 0.075–0.18, 0.025–0.075; 0.075–0.18 and 0.025–0.075 passed the test at the 0.05 and 0.01 significance levels, respectively, the correlation coefficient of negative correlation is −0.18–−0.075, −0.075–−0.025, −0.025–0, −0.18–−0.075, and −0.075–−0.025, respectively, passed the test at the 0.05 and 0.01 significance levels. (2) Perform spatial visualization analysis on sites with significant positive and negative correlations to determine the specific location and distribution range. (3) From the moving time series of 0–12 months, analyze the maximum time delay under different time lags. Time-lag correlation and extent change of strong (weakest) ENSO and drought. Fifth, to study the time-delay relationship between ENSO events defined by different ENSO indexes and the intensity of meteorological drought, we select the strongest and weakest ENSO events as defined by the most suitable index for identifying ENSO events. We shifted Niño3.4, MEI, and SOI from 1 to 12 months for the time-series analysis when calculating the correlation coefficients between climate indices (Niño3.4, MEI, and SOI) and drought indices (SPEI) of 577 meteorological stations in the time period of 1980–2018. The correlation coefficient of each station is counted, and the results are spatially visualized and analyzed. We thus determine whether a lag exists between ENSO events of different intensities and variations in meteorological drought.

3. Results

3.1. Quantifying the Teleconnection Relationship between ENSO Indexes and SPEI in China

We use the three ENSO indexes Niño3.4, MEI, and SOI to correlate with the SPEI from 577 meteorological stations. The results show that all three ENSO indexes (Niño3.4, MEI, and SOI) correlate significantly with meteorological drought, but the correlation strength differs between ENSO indexes (Niño3.4, MEI, and SOI), as shown in Figure 1.

Niño3.4 and MEI are similarly correlated with SPEI and more strongly correlated with SPEI than with SOI (see Figure 1). The Niño3.4 and MEI indexes both show a strong positive correlation with the SPEI to the south of the subtropics, in the lower Yangtze River and to the south of the Yangtze River, and the highest correlation coefficient between SPEI and Niño3.4 and MEI is 0.45. To the north of the subtropics, the correlation between Niño3.4, MEI, and SPEI weakens, whereas a significant correlation appears in the lower Yellow River basin and in arid and semi-arid regions, with a maximum correlation coefficient of 0.38. SPEI has a weak negative correlation with SOI, although, in the central and southern subtropics, their correlation is stronger. By analyzing the Person correlation between Nino3.4, MEI, and SOI, we obtain that the correlation coefficient between MEI and Nino3.4 is 0.892, the correlation coefficient between MEI and SOI is −0.782, and the correlation coefficient between Nino3.4 and SOI is −0.726. So Niño3.4 and MEI are similarly correlated with SPEI and more strongly correlated with SPEI than SOI, and Nino3.4 and MEI have similar correlation ranges to SPEI, respectively.

These findings are consistent with those of Zhang et al. and Yan et al., who concluded that the Niño3.4 index accurately reflects the occurrence and development of ENSO. Considering the various impacts of the different types of El Niño/La Niña events on the climate over China, the identification of varying intensities of El Niño/La Niña events could improve the operational capability of drought monitoring [43,45]. Therefore, Niño3.4 and MEI are suitable indicators to characterize ENSO for the identification and prediction of drought in China.

3.2. Identification of ENSO of Different Intensities

The results presented in Section 3.1 indicate that drought in China is more sensitive to Niño3.4 and MEI, so these indexes are more suitable for identifying ENSO events. Therefore, to confirm the strongest and weakest ENSO events from 1980 to 2018 are identified by using Niño3.4 and MEI, respectively. The Niño 3.4 index and MEI are used to identify the duration and strength of El Niño and La Niña from 1980 to 2018 according to the NCC/CMA criteria. The results show that, from 1980 to 2018, the number of strong and very strong El Niño events is significantly greater than that of La Niña.

ENSO was identified by using Niño 3.4 with a periodicity of 1–3 years and a duration of 5–32 months. From January 1980 to December 2018, there were 11 El Niño and 11 La Niña events. The El Niños that occurred from April 1982 to June 1983, from April 1997 to April 1998, and from October 2014 to April 2016 had peak intensities above 2 and were very strong El Niños, whereas the El Niños that occurred from August 1986 to February 1988, from May 1991 to June 1992, from May 2002 to March 2003, and from June 2009 to April 2010 with peak intensities above 1.5 were strong El Niños. The El Niño from April 1997 to April 1998 lasted 13 months and peaked at 2.7; this was the strongest El Niño. The El Niño from July 2004 to January 2005 lasted 7 months and had the lowest strength with a peak of 0.8. These results are consistent with those of Ren et al. (Ren H.et al., 2018a). La Niña during this period was relatively weak. The La Niñas from April 1988 to April 1989, from June 2007 to May 2008, and from May 2010 to April 2011 exceeded –1.5 and were strong La Niñas (see

Table 3. Identification of El Niño and La Niña from 1980 to 2018 using the Niño3.4 index and the NCC/CMA criteria.

ENSO	Time Span	Duration (Months)	Strength	ENSO	Time Span	Duration (Months)	Strength
El Niño	April 1982–June 1983	15	2.7	La Niña	October 1984–June 1985	9	−1.2
	August 1986–February 1988	19	1.9		April 1988–April 1989	13	−1.5
	May 1991–June 1992.	14	1.9		July 1995–February 1996	8	−1.2
	September 1994–March 1995	7	1.3		June1998–January 2001	32	−1.6
	April 1997–April 1998	13	2.7		October 2005–February 2006	5	−0.8
	May 2002–March 2003	11	1.6		June 2007–May 2008	12	−1.7
	July 2004–January 2005	7	0.8		October 2008–February 2009	5	−0.8
	August 2006.–January 2007	6	1.1		May 2010–April 2011	12	−1.6
	June 2009–April 2010	11	1.7		June 2011–February 2012	9	−0.8
	October 2014–April 2016	19	2.6		July 2016–November 2016	5	−0.66
	September 2018–May 2019	9	1.4		September 2017–February 2018	6	−0.82

).

Based on the NCC/CMA criteria, 11 El Niños and 9 La Niñas were identified between January 1980 and December 2018 when using the MEI, with ENSO having a periodicity of 1–5 years. The peak strength of El Niño occurring from June 1982 to July 1983 and from May 1997 to May 1998 exceeded 2, so these were very strong El Niños, while the peak strength of El Niño occurring from September 1992 to November 1993 and from May 2015 to May 2016 exceeded 1.5 and was a strong El Niño. Among them, from May 1982 to June 1983 El Niño event lasted 14 months and was a stronger El Niño for the period 1980–2018, with a peak strength of 2.1, whereas from August 2006 to January 2007 El Niño event was the weakest in the period 1980–2018, with a peak strength of 0.94. From June 2010 to March 2012 La Niña event peaked at −1.5, which is a strong La Niña (see

Table 4. Identification of El Niño and La Niña from 1980 to 2018 using the MEI and the NCC/CMA criteria.

ENSO	Time Span	Duration (Months)	Strength	ENSO	Time Span	Duration (Months)	Strength
El Niño	June1982–July 1983	14	2.11	La Niña	February 1985–June 1985	5	−0.76
	July 1986–January 1988	19	1.18		June 1988–October 1989	17	−1.22
	September 1991–July 1992	14	1.39		August 1995–August 1996	13	−0.74
	September 1992–November1993	15	1.87		July 1998–July 2000	25	−1.22
	July 1994–February 1995	8	0.94		October 2000–June 2001	9	−0.74
	May 1997–May 1998	13	2.10		October 2005–April 2006	7	−0.67
	August 2002–March 2003	8	0.77		June 2007–May 2009	24	−1.01
	August 2006–January 2007	6	0.67		June 2010–March 2012	22	−1.50
	October 2009–April 2010	7	0.96		July 2017–June 2018	12	−0.77
	May 2015–May 2016	13	1.71

). These results are consistent with those of Lü et al. [46].

3.3. Impact of ENSO Strength on Drought in China

Due to the drought in China is more sensitive to Niño3.4 and MEI, so these are suitable indexes for identifying ENSO events. Therefore, to confirm the strongest and weakest ENSO events, we use Niño3.4 and the MEI to identify the strongest and weakest ENSO events that occurred in the period 1980–2018. When using the Niño3.4 index to define El Niño, the strongest El Niño occurred from April 1997 to April 1998. The weakest El Niño event occurred from July 2004 to January 2005. The strongest and weakest El Niños have different effects on the extent of drought in China, which depends strongly on the time of occurrence, end time, and strength of ENSO. A time-lag correlation analysis between SPEI and El Niño events of varying intensity (this paper considers the strongest and weakest El Niño events) reveals that the time lag between meteorological drought and El Niño depends strongly on the El Niño strength. A time lag of 0–12 months elapses between the strongest ENSO event and the subsequent drought. Two hundred and nine meteorological sites correlate significantly with the strongest ENSO event, most of which are positive correlations. The largest correlation coefficient is 0.49, and the time lag gradually increases from south to north, as shown in Figure 2.

When the strongest El Niño event occurred (from April 1997 to April 1998), the drought response was strong, and the range of drought gradually expanded westward from the southeastern coastal area of China to the middle temperate zone. The time lag for the drought in Alxa and Hexi Corridor (IID2), Junggar Basin (IID3), Altai Mountain and Tacheng Basin (IID4), Ili River Basin of Arid Region (IID5) relates to the strongest El Niño event. When the strongest El Niño event occurred, the drought in China ranged from tropical regions in southern China, such as the Hilly Plain of Fujian, Guangdong, and Guangxi, Mountain Hills in Central Yunnan, and extended northward to the cold temperate regions of China. These results are indicative of a significant relationship between droughts in arid and semi-arid areas from the southeastern coastal areas of China to inland areas in response to the strongest El Niño events. Thus, the strongest El Niño events can lead to droughts in arid tropical, temperate, and inland arid areas in China, as shown in Figure 2.

When the time lag between drought and the strongest El Niño event is 3 months, the drought-prone areas in China gradually shift from the southern tropical region of VIIA2 to the northern cold temperate zone. Droughts expanded in other regions, such as Central Songliao Plain and most of Inner Mongolia, such as the Western Inner Mongolia Plateau and Jiaodong Mountain Hills in Eastern Liaoning Province (IIIA1). In the seventh month of the strongest El Niño event, the drought ranged from the coastal areas of China to the westernmost inland areas, such as the edge of the Tarim Basin in China and the Alxa and Hexi Corridor (IID2), Junggar Basin (IID3), Altai Mountain and Tacheng Basin areas (IID4). From the 10th to the 12th month of the strongest El Niño event, the drought ranged from 30° N to 35° N in China.

A 0–12-month lag occurs between SPEI and the weakest El Niño (from July 2004 to January 2005), as shown in Figure 3. Compared with the strongest El Niño event, the range of the weakest El Niño event affecting the drought in China is reduced, and the number of significantly related sites is reduced to 178. When the weakest El Niño event occurred, the area of drought in China extended from the southeastern coastal areas to the west to the middle temperate zone. In the southeast coastal area of Sichuan Basin (VA3), Guizhou Plateau (VA4), Mountain and Hills in North China (IIIB3), North China Plain (IIIB2), Mountain and Hills in Central Shandong (IIIB1), Jiaodong Mountain Hills in Eastern Liaoning Province (IIIA1), the drought responded strongly to the weakest El Niño event. In the north-south direction, a drought occurred from the tropical area in the south to the northern end of the mid-temperate zone in the north. In Yunnan Plateau and mid-temperate Alxa and Hexi Corridor (IID2), Altai Mountain, and Tacheng Basin (IID4), the drought responded strongly to the weakest El Niño event. Overall, the area where the drought occurred in China responded more strongly to the strongest El Niño event, so a short time lag is an operative between the strongest El Niño event and China.

When the time lag between drought and the weakest El Niño is 0–3 months, the maximum number of meteorological stations reported a significant correlation between the El Niño index and the SPEI, and the SPEI correlates with the Niño3.4 index mostly significantly and positively. These meteorological stations are mainly concentrated in subtropical and mid-temperate coastal areas. From the 4th month to the 10th month of the weakest El Niño event, the number of meteorological stations with a significant correlation between the El Niño index and the SPEI decreases, and the drought range gradually narrows. The exponential correlation decreases, and the two are mostly related by a significant positive correlation (α = 0.05). From the 11th month to the 12th month of the development of the weakest ENSO event, the correlation between the Niño3.4 index and SPEI is weak, and drought occurs only in the cold temperate zone and at a few meteorological stations in the Tarim Basin, Figure 3.

Using the Niño3.4 index to identify the strongest and the weakest El Niño events, we shifted Niño3.4 index from 1 to 12 months for the time-series analysis when calculating the correlation coefficients between climate indices and drought indices to determine how the strength of El Niño events affect the drought in China. Analyzing how El Niño events of different intensities affect the drought in China shows that, for a given site, the response time of drought to the strongest El Niño event is 1–8 months shorter than that for the weakest El Niño event. These sites are concentrated in the mid-temperate zone and in the south. The time lag between the strongest El Niño and drought in Zhuhai Station, Zhongshan Station, and Zhenping Station is 1 month shorter than the time lag between the weakest El Niño and drought. The time lag between the strongest El Niño and drought of Lanzhong station, Haiyuan station is 2 months shorter than the time lag between the weakest El Niño and drought. The time lag between the strongest El Niño and the drought of Luochuan Station is 3 months shorter than the time lag between the weakest El Niño and drought. Fujan Station, Mingshui Station, Xuchang Station, and Wudaoliang Station happened drought after the strongest and weakest El Niño events; the time lag between the strongest El Niño and drought of these stations is 4 months shorter than the time lag between the weakest El Niño and drought. The time lag between the strongest El Niño and drought of Shenyang Station, Shangqiu Station, and Sha Che Station is 5 months shorter than the time lag between the weakest El Niño and drought. The time lag between the strongest El Niño and drought at Guangyuan Station is 6 months shorter than the time lag between the weakest El Niño and drought. The time lag between the strongest El Niño and drought at Zhalantun Station, Chuzhou Station, Wuhu is 7 months shorter than the time lag between the weakest El Niño and drought. The time lag between the strongest El Niño and drought at Manzhouli Station is 8 months shorter than the time lag between the weakest El Niño and drought. In the above cases, the drought-response time for the strongest El Niño event was shorter than that for the weakest El Niño event, Figure 4.

When using the MEI to identify ENSO events, the strongest El Niño occurred from May 1999 to May 1998. The weakest El Niño occurred from August 2006 to January 2007. We shifted MEI from 1 to 12 months for the time-series analysis when calculating the correlation coefficients between climate indices and drought indices and found an obvious time lag between them. A 0–7-month time lag exists between ENSO events and drought, although the range of drought differs. For the ENSO event, the drought range extends from the Pearl River Delta region in eastern China to the mid-temperate region along the east-west direction in China. In the south-to-north direction, drought occurs from the tropics to the northern end of the mid-temperate zone, Altai Mountain and Tacheng Basin (IID4), as shown in Figure 5. The strongest correlation is between El Niño events and droughts with a time lag of 2 months, only a small number of meteorological sites have a time-lag correlation between SPEI and MEI, and the range of drought spreads gradually from the Pearl River Delta region to north of China. From the third to fifth month of the strongest El Niño event, the drought range gradually expands from the southeast coastal area to the northwest and northeast region. The drought in the Turi River of the northwest region has a more significant time-lag correlation with the weakest El Niño event. In the fourth month of the El Niño event, the westernmost edge of the drought-affected region in the Tarim Basin. In the sixth and seventh months of the strongest El Niño event, droughts begin in North China Plain (IIIB2).

Using the MEI, the weakest El Niño event extends from August 2006 to January 2007. Compared with the strongest El Niño event, the weakest El Niño event produces a smaller drought region, with the drought ranging from 30° N to 45° N in the coastal areas of China in the east and to the mid-temperate Tarim Basin in the west. During the development of the weakest ENSO event, the impact of drought in China is relatively scattered. The strongest correlation is between El Niño events and droughts with a time lag of 3–5 months, and the drought range gradually expands from the south subtropical region to the north and northwest regions (see Figure 6).

4. Discussion

ENSO has been linked to climate anomalies throughout the world. ENSO is the strongest climatic signal produced by the global ocean–atmosphere interaction and has a cycle of 2–7 years. The teleconnection between ENSO and climate is the scientific basis of long-range weather forecasts provided by the meteorological agencies of several countries [20]. The warm phase of ENSO is called El Niño, and the cold phase is called La Niña. Distinguishing the phases of ENSO and identifying their duration and strength is important for analyzing possible correlations between ENSO and extreme weather. Indexes commonly used to classify ENSO include the SOI, which is based on differences in sea level pressure measured in the tropical Pacific Ocean and regional SST indexes such as Niño1+2, Niño3, Niño4, and Niño3.4 [47]. In addition to these indicators, the trans-Niño index and the MEI are also used extensively to identify ENSO.

Currently, no consensus exists regarding which index most accurately describes and identifies ENSO. By comparing the SOI, Niño1+2, Niño3, Niño4, Niño3.4, and MEI indexes, Hanley et al. reported that the Niño 3.4 index accurately identifies and describes ENSO but that the SOI is more reliable. Over a relatively short time scale (43 years), the MEI can describe and identify ENSO more flexibly [48]. The National Standard for El Niño-La Niña Event Identification developed by the National Climate Center of the China Meteorological Administration states that the Niño3.4 region is the key dynamic region for ENSO, and the Niño3.4 index describes the start time, end time, duration period, and strength of ENSO, which are the ideal parameters for ENSO indicators for monitoring the climate of China [20,49].

As the strongest global ocean–atmosphere interaction, ENSO redistributes heat and moisture by altering large-scale airflow at the surface and small-scale ocean circulation, thus significantly affecting temperature and precipitation over a wide area in the middle and low latitudes, which allows ENSO to generate climate anomalies over three-quarters of the surface of the globe [3,50]. The different stages of development, strength, starting time, and location of ENSO produce different effects on climate in China, even if the same ENSO event affects different regions or different seasons in the same region [49]. An El Niño year in China has high temperatures in the winter and spring and low temperatures in the summer and autumn; the national precipitation is low, the frequency of cold and frost damage increases in northeast China, and drought happen more often in northern China (especially in the Yellow River basin) and northwest China [44,51]. In contrast, in La Niña years, the temperature is low in the winter and spring and high in the summer and autumn, and the probability of flooding in the northwest increases due to the concentration of heavy rainfall in China [4,52].

Different intensities of ENSO events have different effects on air temperature and precipitation. When a super El Niño event occurs, it prolongs the effect and increases the degree of dry and wet climate change in China. A clear lag exists between super El Niño events and variations in precipitation [37]. Strong ENSO events rapidly cause high-intensity circulation and precipitation anomalies and trigger extreme climate, such as intense droughts, whereas the effects of moderate-strength ENSO events become clear only in August. Strong ENSO events not only directly affect the East Asian summer wind circulation and the summer precipitation in China but also control other factors, such as the changes in the Southern Hemisphere circulation [20]. El Niño events of varying intensities produce complex effects on the temperature in various vegetation regions in China [53]. The average maximum daily temperature in most areas of China (except for the humid mid-temperate and semi-arid mid-temperate regions in autumn) is caused more by strong El Niño events than in normal years. Whereas both weak El Niño events and weak La Niña events have an insignificant effect on the average daily maximum temperature in China, moderate El Niño causes a significant decrease in the average maximum daily temperature in the spring in the semi-arid regions of the middle temperate zone in China.

When strong El Niño events happen, they cause an increase of 1.63 °C in the average maximum daily temperature in the spring in the arid regions of the middle temperate zone. When the strongest El Niño event happens, it causes a significant increase of 2.24 °C in the average maximum daily temperature in the spring in the arid regions of the middle temperate zone [10,28,54]. Studies show that ENSO events of different intensities produce different effects on precipitation and temperature. Stronger ENSO events, implying a stronger signal, improve the predictability of climate factors, so it is important to clearly determine the strength of ENSO events for practical predictions. Therefore, the strength of ENSO must be clearly distinguished in the actual predictions [11,33,55,56]. Moreover, Xing et al. potentially plausible atmospheric mechanisms for the changes in the lagged influences of ENSO-drought with respect to the changed time lags have been explored by using the monthly, 850 hPa vertical, zonal, and meridional wind speeds and directions of the NCEP/NCAR reanalysis. The likelihood of drought occurrence is highest during cool phases of ENSO (CEN), followed by warm ENSO (WEM); the influence of WEN/WEM on droughts is statistically significant mostly for a time lag of 10 months, whereas CEN/CEM shows no lagged influence. Furthermore, the variability of the 850 hPa wind anomalies during different ENSO episodes at various time lags corresponds to the lagged influence of ENSO on droughts [57]. Niño Southern Oscillation (ENSO) was closely related to the variations of drought; meanwhile, ENSO has a significant lag time scale cumulative influence on droughts, especially the 15-year sliding effect was the most obvious in China [58]. These studies are important supporting evidence for the conclusions of this paper and demonstrate that ENSO anomalies are early warning signals of droughts and hence can be beneficial for increasing drought predictivity.

This paper considers the relationship between drought and circulation factors from a statistical perspective to analyze how El Niño events of various intensities affect the severity of drought in China. The results reveal a time lag between drought and atmospheric circulation, making this an in-depth and complementary study to previous drought research. Due to the differences in research years, data reliability, indicators for classifying ENSO, the activeness of selected ENSO events, and the reliability of predictions of how ENSO impacts the regions, some research results differ. However, many factors must be combined to determine the severity of drought in China because the actual physical mechanisms are complex, such as topography, geomorphology, and vegetation, all of which can affect a drought. Thus, further research is needed to better understand the factors that influence droughts in China.

5. Conclusions

ENSO has been linked to climate anomalies throughout the world and is a strong climatic signal that stems from the mutual coupling of global oceanic and atmospheric circulation. ENSO is understood as an inter-annual, quasiperiodic, coupled mode of the tropical ocean–atmosphere system with worldwide hydroclimatic teleconnections. Decadal variations in ENSO teleconnections to continental temperature, precipitation, stream flow, and drought indexes around the globe have also been noted by many. ENSO events often cause global climate anomalies involving changes in atmospheric circulation that lead to local climate anomalies. ENSO affects China’s climate through changes in atmospheric circulation and produces different effects on China’s climate from coastal regions to the interior.

The Niño3.4 index and MEI are used to identify the duration period, strength, and onset of El Niño and La Niña from 1980 to 2018. A strong asymmetry exists between the two phases of El Niño and La Niña, with a higher number of strong El Niño events being recorded. La Niña events are significantly less intense than El Niño events, especially for the stronger events. ENSO events are identified by using Niño3.4 and have a period of 1–3 years and a duration of 5–32 months. The El Niño events that occurred from April 1982 to June 1983, from April 1997 to April 1998, and from October 2014 to April 2016 were the strongest El Niño events. La Niña events are relatively weak, with no super events since 1980. A strong La Niña occurred from April 1988 to April 1989, from June 2007 to May 2008, and from May 2010 to April 2011. From 1980 to 2018, the periodicity of ENSO events identified by using the MEI index was 1–5 years, and the strongest El Niño events happened from June 1982 to July 1983, from May 1997 to May 1998, and from September 1992 to November 1993. A strong El Niño event lasted from May 2015 to May 2016, and a strong La Niña event lasted from June 2010 to March 2012.

We quantify the teleconnection response between different ENSO indexes and the SPEI, evaluate the temporal and spatial distribution of the correlation coefficient between ENSO indexes and the SPEI and analyze ENSO of different intensities (from the strongest El Niño event to the weakest El Niño event) affect drought in different regions of China. A significant correlation exists between ENSO indexes and the SPEI. Due to differences in the calculation of different ENSO indexes, the correlations between Niño3.4, MEl, SOI, and SPEI all differ. In particular, the response varies between Niño3.4, SOI, MEI, and SPEI. Niño3.4 is most strongly correlated with SPEI, followed by MEI. SOI and SPEI are most poorly correlated, and the correlation is negative. The response of ENSO indexes to the SPEI is strongest in the south of the subtropical region, in the lower reaches of the Yangtze River, and in the southern region of the Yangtze River. To the north of the subtropical region, the correlation between Niño3.4, MEI, and SPEI weakens, but a significant correlation occurs in the lower reaches of the Yellow River and in arid and semi-arid regions. Therefore, southern China, the Jiangnan region, the middle and lower reaches of the Yangtze River, and the arid and semi-arid regions of northwestern China are all drought-sensitive areas susceptible to ENSO, with areas closer to the ocean having stronger correlations.