Spatiotemporal Variation Characteristics of Precipitation in the Huaihe River Basin, China, as a Result of Climate Change

Abstract

Climate change is a global scientific problem, and its impact on the spatiotemporal variations of precipitation has been a crucial research topic. Although previous studies have used different methods to evaluate the precipitation characteristics of the Huaihe River Basin, the time series were short and the few stations could not fully and accurately represent the precipitation characteristics. In this study, daily temperature and precipitation data were collected from 233 meteorological stations in the Huaihe River Basin from 1960–2020. The Mann–Kendall test was used to analyze the trend and significance of interannual and interseasonal scale changes in temperature and precipitation in the Basin, respectively. The correlation between temperature and precipitation was analyzed using the Pearson correlation coefficient method. The spatial distribution of the significance of temperature and precipitation changes and the spatial distribution of the correlation between temperature and precipitation in the basin were plotted. The temperature in the basin tended to increase on interannual and interseasonal scales, with more noticeable changes in spring and winter. Precipitation showed an overall decreasing trend but an increasing trend in localized areas in the south. A decreasing trend in the interseasonal variation scale was observed in spring, an increasing trend in winter, a decreasing trend in the northeastern region in summer, an increasing trend in the southwestern region, and an increasing trend in the northern and southern parts in autumn were observed. The correlation between average temperature and precipitation on interannual and interseasonal scales was analyzed using Pearson’s correlation coefficient method, and the annual average temperature and annual average precipitation in the Huaihe River basin were found to be negatively correlated, except for sporadic areas that showed extremely weakly positive correlations or no correlations.

1. Introduction

In recent years, climate change has become a topic of extensive discussion among researchers worldwide. The globe is changing at a rate that has not been seen in thousands of years due to anthropogenic activity, rising temperatures, rapid changes in the atmosphere, oceans, and polar regions and increasing weather extremes around the world [1,2,3].

Owing to the impact of global climate change, China’s climate has undergone some changes, among which the trend of increasing average temperature is more evident. From 1909–2011, the average warming rate in China was higher than the global average, reaching 0.9–1.5 °C [4]. From 1960–2018, the average annual temperature in China increased, reaching an average warming of ~0.278 °C per decade. Wang et al. (2006) reported that the second half of the 20th century was the most significant period of global warming in the last millennium, during which the global annual precipitation decreased by 0.54 mm/year—with the 1950s being the period with high precipitation—and a clear downward trend was observed from the 1960s to the 1990s; however, a rebound occurred in the late 1990s [5]. Zhao et al. (2020) pointed out that from 1960–2018, precipitation showed an increasing trend in the northwest and southeast regions, while there was a decreasing trend in the area near the northeast-southwest oriented ecological transition zone [6]. The concentration of precipitation significantly decreased in all ecological zones except the humid subtropical and humid tropical areas, and the intra-annual variation of precipitation was relatively balanced.

The Huaihe River Basin is a significant geographical region in China, located in the north-south climate transition zone. The northern part of the Huaihe River is located in the warm temperate zone, while the southern part of the Huaihe River is in the northern subtropical zone. Winter and spring are dry seasons with low precipitations, whereas summer and autumn are characterized by high temperature and precipitation, with sharp shifts in temperature, along with droughts and floods, and are highly sensitive to global climate change [7]. Numerous scholars have investigated the causes of precipitation in the Huaihe River Basin using different means and methods. Using rainfall data from 1961–2005, Lu et al. (2011) [8] and Zheng et al. (2015) [9] analyzed and found that the interannual oscillations in the Huaihe River Basin were more intense during this period, and the annual precipitation showed a decreasing trend, with different trends in each season; however, none of them reached a significant level. Wang et al. (2012) statistically analyzed the observation data from 170 ground-based meteorological stations in the Huaihe River basin and found that the annual average temperature in the Huaihe River basin increased significantly from 1961–2008, with the greatest warming in winter [10]. The precipitation in autumn decreased from the 1990s, and the annual precipitation increased significantly after 2000. The precipitation in summer increased, with no significant changes in summer and winter. Wang et al. (2017) used the Mann–Kendall test and Kriging interpolation to analyze precipitation data from 1960–2014 in the Huaihe River basin, and the results revealed a slowly decreasing trend in the total precipitation and the number of days with heavy rainfall [11]. In an analysis of the precipitation characteristics of the Huaihe River basin over the past 58 years using statistical methods such as Spearman rank order correlation and paired slope regression median, Zhao et al. (2018) pointed out that the annual precipitation in the Huaihe River basin varied significantly from north to south, but the overall annual precipitation showed a slightly decreasing trend [12]. Based on climate data from seven hydrological stations in the Huaihe River Basin of the Anhui Province from 1955–2011, Zhou et al. (2014) used R/S analysis to analyze the average temperature and precipitation in the region over the past 56 years [13]. Spatial differences exist in the distribution of temperature and precipitation, with the overall characteristics of high temperature and high precipitation in the southwest, low temperature and low precipitation in the northeast, and medium temperature and low precipitation in the central region. Using the defined annual precipitation concentration and time period concentration index, Pang (2018) investigated the interannual variability characteristics of the non-uniform intra-annual precipitation distribution in the Huaihe River Basin from 1961–2015 and found that the intra-annual precipitation distribution in the Huaihe River Basin was non-uniform [14].

Based on previous studies on the precipitation characteristics of the Huaihe River Basin, it was found that different methods were used to evaluate the precipitation characteristics from the perspective of different elements; however, in terms of data selection, the time series were short and the few stations could not fully and accurately represent the precipitation characteristics of the entire Huaihe River Basin.

This study considered the Huaihe River Basin as the study area, selected 233 meteorological stations in the basin from 1960–2020, and considered the influence of climate change to study the spatiotemporal variation characteristics of precipitation in this basin.

2. Overview of the Study Area

The Huaihe River Basin is located in China’s Henan, Jiangsu, and Anhui, between the Yangtze and Yellow River basins, at 111°55′ to 121°25′ E and 30°55′ to 36°36′ N, with a basin area of 270 × 10³ km². The watershed begins at Tongbai Mountain, extends to the Fuyu Mountain in the west, the Yellow Sea in the east, the Dabie Mountains, the Jianghuai Hills, the Tongyang Canal and the southern embankment of the Rutai Canal in the south. The north is bounded by the south embankment of the Yellow River and Taishan Mountain, and it is adjacent to the Yellow River basin. The Huaihe River basin is located in the north-south climate transition zone of China. It is a significant national commodity base for grain, cotton, and oil and has a mild and warm temperate semi-humid monsoon climate favorable for agricultural conditions [15]. The topography of the Huaihe River basin generally shows a northwest-to-southeast slope, with low-elevation alluvial plains on both sides of the mainstream of the Huaihe River. To the south of the Huai River, low-elevation alluvial and floodplain terraces are the main landform types, while the central part of the Huaihe River is dominated by low-elevation river floodplains [16]. The overall water system in the Huaihe River basin shows a fan-shaped distribution, and there are various forms of landforms with more obvious geographical differences. Mountainous areas are mainly concentrated in the northeast, west and south. Mountains and hills account for about 32% of the total area, plains account for about 55%, and the remaining 13% are lake depressions [17]. Figure 1 shows the geographical location map of the Huaihe River Basin.

3. Data and Methods

3.1. Data Sources

The data used in this study were provided by the National Meteorological Information Center of the China Meteorological Administration and obtained from 233 meteorological stations. The data series included daily precipitation and average daily temperature data from 1960–2020. Figure 1 shows specific information corresponding to the stations selected for this study. Among the 233 stations in the Huaihe River basin, the missing rate of precipitation stations and temperature stations is about 8% and 12%, respectively. The missing data are mainly missing for one day or several continuous days. The missing data are replaced by the average value of neighboring stations.

Annual and seasonal precipitation data from the stations were accumulated based on daily data. February–April, May–July, August–October and November–December/January correspond to spring, summer, autumn and winter, respectively.

3.2. Research Methods

In this study, the Mann–Kendall test was used to test the temperature and precipitation trend and significance. Pearson’s correlation coefficient was used to analyze the correlation between meteorological elements and precipitation. The spatial distribution of temperature and precipitation changes in the Huaihe River Basin and the spatial distribution of the correlation between temperature and precipitation were plotted.

3.2.1. Mann–Kendall Test

The Mann–Kendall test is a widely used nonparametric statistical analysis method [18,19,20,21,22,23,24]. Its characteristic advantage is that the data samples do not need to obey any specific distribution pattern, are not disturbed by a few outliers, and have a high quantification degree. Furthermore, this test is commonly used to predict long-term trends in hydrometeorological time series data, such as temperature, precipitation, runoff and water quality [25,26,27].

In the Mann–Kendall test, it is assumed that the null hypothesis H0 is the time series data (X₁, X₂, ..., X_n), where n represents the number of independent samples of identically distributed random variables, and has no monotonous trend, while the alternative hypothesis H₁ has a monotonic trend and is a bilateral test. For all k values, j ≤ n and i ≠ j, the distributions of X_i and X_j are different. The test statistic S is defined as:

$$S={\displaystyle \sum _{k=1}^{n-1}{\displaystyle \sum _{j=k+1}^{n}Sgn\left(X_j-X_k\right)}}$$

(1)

where $Sgn\left(\right)$ is the symbolic function:

$$Sgn(X_j-X_k)=\{\begin{array}{l}1\\ 0\\ -1\end{array}\begin{array}{l}{X}_j-X_k>0\\ X_j-X_k=0\\ X_j-X_k<0\end{array}\}$$

(2)

S is normally distributed with a mean of 0 and a variance given by:

$$Var\left(S\right)=\frac{n\left(n-1\right)\left(2n+5\right)}{18}$$

(3)

Further, the formulas for the determination of the M–K statistic, Z, under the conditions S > 0, S = 0, S < 0, are as follows:

$$\mathrm{Z}=\{\begin{array}{l}\left(S-1\right)/\sqrt{Var\left(S\right)}\\ 0\\ \left(S+1\right)/\sqrt{Var\left(S\right)}\end{array}\begin{array}{l}S>0\\ S=0\\ S<0\end{array}\}$$

(4)

For a given significance level α, if $\left|Z_{}\right|\ge Z_{1-\raisebox{1ex}{$\alpha $}\!\left/ \!\raisebox{-1ex}{$2$}\right.}$, then the original assumption is unacceptable. That is, there is a significant increasing or decreasing trend in the time series data above the α significance level. For the statistical variable Z, if it is greater than 0, the series has an increasing trend; if it is less than 0, the series has a decreasing trend [28]. Under the premise of a significance level α = 0.05 (the critical value being ±1.96), there are four kinds of results: significant increase, significant decrease, no significant increase and no significant decrease.

3.2.2. Pearson’s Correlation Coefficient

Pearson’s correlation coefficient is mainly used to measure the degree of correlation (linear correlation) between two variables, X and Y [29,30,31]. The generated Pearson correlation coefficients were distributed in the interval [–1, 1]. In natural sciences, this coefficient is widely used to measure the degree of linear correlation between two variables. It is important to verify whether the data satisfy Gaussianity before the analysis. The non-parametric test (Kolmogorov–Smirnov test and Shapiro–Wilk test) and graphical method (P-P plot and Q-Q plot) are selected for verification separately.

All the time series pass the non-parametric test and graphical method. Taking Shandong Zichuan station as an example, the results of the Gaussianity test using spss software are as follows. The Kolmogorov–Smirnov test result is sig. = 0.2 > 0.05, and the Shapiro-Wilk test result is sig. = 0.343 > 0.05. The graphical method is used to further confirm the results, and the graph shows that the data are basically in the vicinity of a straight line and can be considered Gaussian. The results of the graphical method are shown in Figure 2. The data satisfy Gaussianity, and also, according to the Mann–Kendall test, for a station, temperature and precipitation show a trend (increasing or decreasing), so there exists a linear relationship. The correlation analysis of temperature and precipitation in this paper can be performed by using Pearson’s correlation coefficient.

We also used Spearman’s correlation to verify whether the conclusion is consistent with Pearson’s correlation coefficient.

The correlation between the two variables was determined by the value of ρ (ρ ∊ [−1,1]). The grading criteria of the Pearson correlation degree grading scale are shown in

Table 1. Pearson correlation degree grading scale.

Correlation Coefficient Value	Degree of Correlation
|ρ| = 0	Completely irrelevant
0 < |ρ| < 0.2	Very weakly correlated or uncorrelated
0.20 ≤ |ρ| < 0.4	Weakly correlated
0.40 ≤ |ρ| < 0.6	Moderately correlated
0.60 ≤ |ρ| < 0.8	Strongly correlated
0.80 ≤ |ρ| < 1	Very strong correlation
|ρ| = 1	Completely correlated

.

4. Results and Discussion

4.1. Temperature Trend Analysis and Significance

4.1.1. Analysis of Annual Average Temperature Change

The Mann–Kendall test was conducted on the annual mean temperature of the Huaihe River Basin from 1960–2020, and it was found that the annual mean temperature of the 233 stations showed an increasing trend, among which 228 stations showed a significant increase, accounting for 97.8% of the total stations, and five stations showed an increasing trend, accounting for 2.2% of the total stations.

Combined with the spatial distribution map of Z-values of annual mean temperature (Figure 3), the annual mean temperature in the Huaihe River Basin showed an overall increasing trend. The temperature increases were most drastic in the central and eastern coastal areas, while local areas in the west and southwest exhibited more significant temperature increases.

4.1.2. Analysis of Average Temperature Variation in Different Seasons

The Mann–Kendall test of the average temperature of different seasons from 1960–2020 in the Huaihe River basin showed a significant increase in the average spring temperature at all 233 sites.

The average summer temperature showed an increasing trend at 218 sites, accounting for 93.6% of the selected sites, with 133 sites showing dramatic changes and a significantly increasing trend. Fifteen sites showed a decreasing trend, accounting for 6.4% of the selected sites.

In autumn, the average temperature at 232 sites showed an increasing trend, accounting for 99.6% of the total sites, of which 161 changed drastically and showed a significant increase. One site showed a decreasing trend, accounting for 0.4% of the total sites.

The average winter temperature showed an increasing trend at 230 stations, accounting for 98.7% of the selected stations, of which 201 stations showed a dramatic change and significant increasing trend. Three stations showed a decreasing trend, accounting for 1.3% of the selected stations.

Combined with the spatial distribution map of Z-values of the average temperature in different seasons (Figure 4), the average temperature in the Huaihe River Basin in different seasons showed a significantly increasing trend overall, with variability among different seasons, but the temperature in the eastern and central parts of the Huaihe River Basin was more drastic in seasonal changes and showed a significant increasing trend.

In spring, the Huaihe River Basin had the most noticeable change in temperature rise, except for the western part, where the temperature change was more moderate; however, the other areas had more drastic temperature changes. In summer, the changes were more drastic in the eastern and central parts of the Huaihe River basin and slightly flat in the western parts. In autumn, the changes were more drastic in the northeastern and central parts of the Huaihe River basin, while the other areas were slightly flat. In winter, except for some areas in the northeast and southwest of the Huaihe River basin, the changes were more moderate, while the changes in other areas were more drastic.

In summary, although the overall temperature in the Huaihe River Basin is on the rise, spatiotemporal differences exist in the degree of the increase. The changes in the northern and central-eastern regions showed a significant increase, and the changes in temperature rise were more obvious in spring and winter. They showed significant trends in the northeastern region in summer and autumn, but the changes were more moderate in the central and western regions. Using observational data from 145 ground meteorological stations in the Huaihe River Basin from 1961–2010, Ye et al. (2016) analyzed the spatiotemporal characteristics of conventional meteorological elements in the Huaihe River Basin over the past 50 years [32]. The annual average temperature of the whole basin showed an increasing trend, with significant spatial variability in the annual average temperature change; the South Four Lakes area, south of the middle and lower reaches of the mainstream of the Huaihe River, and the Funiu Mountain area were areas of significant temperature increase. This is consistent with the findings of the present study.

4.2. Precipitation Trend Analysis and Significance

4.2.1. Analysis of Annual Precipitation Changes

The Mann–Kendall test of annual precipitation in the Huaihe River Basin from 1960–2020 revealed a decreasing trend at 121 stations, accounting for 51.9% of the total number of stations, with three stations showing a significant decreasing trend. The annual precipitation at 112 stations showed an increasing trend, accounting for 48.1% of the total number of stations, and two stations showed a significant decreasing trend.

The annual precipitation in the Huaihe River Basin showed an overall decreasing trend, but the overall change was more moderate, with a decreasing trend in the northeast and an increasing trend in the central and southern regions. Using observational data from 145 ground meteorological stations in the river basin from 1961–2010, Ye et al. (2016) analyzed the spatiotemporal characteristics of conventional meteorological elements in the river basin over the past 50 years [32]. The average annual precipitation is bounded by the mainstream of the Huaihe River, showing an increase in the south and a decrease in the north, leading to an increasing trend of spatial distribution differences in the average annual precipitation in the basin from north to south. Chang et al. (2013) calculated the annual scale and seasonal-scale precipitation spacing percentages and the standardized precipitation index in the basin based on the analysis of precipitation changes at 26 meteorological stations in the river basin from 1960–2011 [33]. The results indicate, on the spatial scale, a trend of wetter central and western regions of the basin, and drier eastern coastal regions, with the changing trend indicating approximate vertical zoning. The findings of Ye Jinyan and Chang Shuaipeng regarding precipitation trends in the Huaihe River Basin are consistent with the results of the analysis in this thesis. The spatial distribution of Z-values of annual precipitation in the Huaihe River basin is shown in Figure 5.

4.2.2. Analysis of Precipitation Variation in Different Seasons

Mann–Kendall tests were conducted for spring, summer, autumn and winter precipitation in the Huaihe River basin station from 1960–2020, and an overall decreasing trend was observed in spring and autumn precipitation, an overall increasing trend in winter precipitation, and basically equal areas with increasing and decreasing trends in summer. The table of precipitation trends in different seasons in the Huaihe River Basin provides more information (

Table 2. Precipitation trends for different seasons in the Huaihe River Basin.

Season	Decreasing Trend			Increasing Trend
	Number of Stations	Percentage (%)	Number of Significant Changes	Number of Stations	Percentage (%)	Number of Significant Changes
Spring	216	92.7	13	17	7.3	0
Summer	117	50.2	7	116	49.8	2
Autumn	128	54.9	9	105	45.1	0
Winter	12	5.2	4	221	94.8	25

).

Spring precipitation in the Huaihe River Basin showed an increasing trend in the northeastern, northern and part of southern regions, but the changing trend was not significant; in the central and western areas, the precipitation showed a decreasing trend, and the changing trend in the western part was more significant (Figure 6a).

Summer precipitation in the Huaihe River Basin showed an increasing trend in the southwestern and central regions, but the trend was less significant. Precipitation in the northeastern part of the region showed a decreasing trend, and the trend in the northern part was more significant (Figure 6b).

Autumn precipitation in the Huaihe River Basin showed an increasing trend in the part of northern and southern regions, but the changing trend was not significant. Precipitation in the western and eastern regions and part of the northern regions showed a decreasing trend, and the changing trend in the part of the eastern region was more significant (Figure 6c).

Winter precipitation in the Huaihe River Basin showed an increasing trend in the central region, with significant changes in the part of the southeast. The precipitation in the part of northeast and southwest showed a decreasing trend, with more significant changes in the west and part of the north (Figure 6d).

In summary, precipitation in the Huaihe River Basin showed an overall decreasing trend but increased in parts of the south. In spring, the overall decreasing trend was dominant, while the areas in the northeast, north and part of the south showed an increasing trend. In summer, there were significant differences between the east and west regions, with decreasing trends in the northeastern region and increasing trends in the southwestern region. In autumn, an increasing trend was mainly observed in the northern and southern parts. In winter, a decreasing trend was mainly observed in the northeast and southwest, while an increasing trend was observed in the central region.

4.3. Pearson’s Correlation Analysis

The temperature data and precipitation data satisfy Gaussianity by using Kolmogorov–Smirnov test and Shapiro–Wilk test, P-P plot and Q-Q plot in Spss software. By analyzing and calculating the $\rho$ values for each station, the correlation between annual average precipitation and the annual average temperature at the selected stations at interannual and interseasonal scales can be clearly found and ranked. The results are shown in the table of the distribution of the number of stations corresponding to the $\rho$ value in the Huaihe River Basin (

Table 3. Distribution of the number of stations corresponding to the $\rho$ -value in the Huaihe River Basin.

|ρ| Value Range	Nature	Spring		Summer		Autumn		Winter		Interannual Correlation
		Positive	Negative	Positive	Negative	Positive	Negative	Positive	Negative	Positive	Negative
|ρ| = 0	Completely irrelevant	0	0	0	0	0	0	0	0	0	0
0 < |ρ| < 0.2	Very weakly correlated or uncorrelated	10	134	0	55	40	108	110	63	29	146
0.2 ≤ |ρ| < 0.4	Weakly correlated	0	88	0	154	0	72	12	45	0	55
0.4 ≤ |ρ| < 0.6	Moderately correlated	0	1	0	24	0	12	0	3	1	1
0.6 ≤ |ρ| < 0.8	Strongly correlated	0	0	0	0	0	1	0	0	0	1
0.8 ≤ |ρ| < 1	Very strong correlation	0	0	0	0	0	0	0	0	0	0
|ρ| = 1	Completely correlated	0	0	0	0	0	0	0	0	0	0

).

We also used Spearman’s correlation to perform correlation analysis. The results are as follows: In spring, there were 136 with extremely weakly negative or no correlation, 81 with weak negative correlation and 16 stations with extremely weakly positive or no correlations. In the summer, 14 stations were extremely weakly negative or no correlations, 151 with weak negative correlations, 67 with moderate negative correlations and 1 station with extremely weakly positive or no correlations. In autumn, 77 stations showed extremely weakly negative or no correlations, 127 with weakly negative correlations, 19 with moderate negative correlations and 10 stations showed extremely weakly positive or no correlations. In winter, there were 69 stations with extremely weakly negative or no correlations, 40 stations with weak negative correlations, 5 stations with moderate negative correlations and 116 sites with extremely weakly positive or no correlations and 3 sites with weak positive correlations. The annual mean temperature and average annual precipitation had 133 stations with extremely weakly negative or no correlations, 76 stations with weak negative correlations, 9 stations with moderate negative correlations, and 14 sites with extremely weakly positive or no correlations and 1 site with weak positive correlations. The results of Spearman’s correlation analysis are basically consistent with Pearson’s correlation analysis.

4.3.1. Correlation Analysis of Mean Annual Temperature and Annual Precipitation

As can be seen from Table 3, the annual mean temperature and average annual precipitation in the Huaihe River Basin had 203 stations with negative correlations, including 146 extremely weakly negative or no correlations, 55 weak negative correlations, one moderate negative correlation and one strong negative correlation, accounting for 87.1% of the total number of stations. There were 30 stations with positive correlations, accounting for 12.9% of the total number of stations, of which 29 had very weak positive correlations or no correlations and one had a moderate positive correlation. Overall, the annual mean temperature and precipitation in the Huaihe River Basin were negatively correlated. Combined with the spatial distribution of Pearson’s interannual correlation results (Figure 7), the correlation distribution of the annual mean temperature and annual precipitation in the basin was relatively concentrated. The contiguous belt area from northeast to southwest showed a very weak negative correlation, the northwest area showed a weak negative correlation and the southeast and part of the north showed a very weak positive correlation.

4.3.2. Correlation Analysis of Average Temperature and Precipitation in Different Seasons

The average temperature and precipitation in the Huaihe River Basin in spring, summer and autumn were negatively correlated. There were 223 stations with negative correlations in spring, including 134 with extremely weakly negative or no correlation, 88 with weak negative correlation and one with a moderate negative correlation, accounting for 95.7% of the total number of stations. There were 10 stations with extremely weakly positive or no correlations, accounting for 4.3% of the total number of stations. In the summer, 233 stations were negatively correlated, including 55 with extremely weakly negative or no correlations, 154 with weak negative correlations and 24 with moderate negative correlations. In autumn, 193 stations showed negative correlations, including 108 with extremely weakly negative or no correlations, 72 with weakly negative correlations, 12 with moderate negative correlations and one with a strong negative correlation, accounting for 82.8% of the total number of stations; 40 stations showed extremely weakly positive or no correlations, accounting for 17.2% of the total number of stations. The positive and negative correlations of the mean temperature and precipitation in winter were relatively balanced, but the correlations were low. There were 111 stations with negative correlations in winter, including 63 with extremely weakly negative or no correlations, 45 with weak negative correlations and three with moderate negative correlations, accounting for 47.6% of the total number of stations. There were 122 sites with positive correlations, including 110 sites with extremely weakly positive or no correlations and 12 sites with weak positive correlations, accounting for 52.4% of the total number of sites.

In terms of spatial distribution (Figure 8), the average temperature and precipitation in the Huaihe River Basin in spring, summer and autumn had an overall negative correlation. The average temperature and precipitation in winter had a more balanced positive and negative correlation, but both correlations were low. Pearson correlations differed greatly between the seasons. In spring, the Huaihe River basin showed an extremely weakly negative correlation from the northeastern to southern contiguous areas and a weak negative correlation in the part of the western area. In summer, the whole Huaihe River basin was weakly negatively correlated, with extremely weakly negative correlations in the northern, central and parts of the western area and moderately negative correlations in the parts of the southern area. In autumn, the eastern part of the Huaihe River Basin showed an extremely weakly positive correlation, the northeastern and part of the central area showed an extremely weakly negative correlation and part of the western area showed a weak negative correlation. In winter, the central-eastern to eastern parts of the Huaihe River basin showed an extremely weakly positive correlation, its parts showed a weak positive correlation, the central-western parts showed an extremely weakly negative correlation, the western parts showed a weak negative correlation and the sporadic parts showed a moderate negative correlation.

5. Conclusions

In this study, the trend and significance analysis of temperature and precipitation at interannual and interseasonal scales were performed by Mann–Kendall test and the correlation between temperature and precipitation was analyzed using Pearson’s correlation coefficient and the following conclusions were obtained:

(1) Annual and seasonal mean temperatures in the Huaihe River Basin showed an increasing trend. The temperature increases in spring and winter were more noticeable, and the trend was significant in the northeast in summer and autumn; however, the changes were more moderate in the central and western parts of the region. The degree of temperature change is influenced by geographical location and varies widely in space. The northern and central-eastern parts of the Huaihe River Basin showed a significant increasing trend in interseasonal and interannual variations. Although other regions showed slightly different results in different seasons, the overall trend was still increasing.

(2) Precipitation in the Huaihe River Basin showed an overall decreasing trend, but in the southern part, local precipitation showed an increasing trend. The overall decreasing trend was dominant in spring. In winter, the trend was increasing. In summer, the difference between east and west was evident, with a decreasing trend in the northeast area and an increasing trend in the southwest area and the ratio of the increasing trend to the decreasing trend was approximately 1:1. In autumn, the increasing trend was mainly in the north and parts of the south.

(3) The correlation between mean temperature and precipitation on interannual and seasonal scales was analyzed using Pearson’s correlation coefficient method, and the annual mean temperature and precipitation in the Huaihe River basin were negatively correlated, except for sporadic areas that showed extremely weakly positive or no correlations. The average temperature and precipitation in spring, summer and autumn showed negative correlations overall, but the correlations were weak. The average temperature and precipitation in winter showed positive and negative correlations in basically equal amounts.