The increase of temperature and precipitation in the different regions of Tarim River Basin has spatial and temporal heterogeneity over 1961-2021.

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Siqi Wang, Aihaiti Ailiyaer, Mamtimin Ali, Peng Jian, Yongqiang Liu, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4717419/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Based on the monthly temperature and precipitation observation data of 42 national meteorological stations in the Tarim River Basin (TRB) from 1961 to 2021, the spatiotemporal variation characteristics and differences of temperature and precipitation in the whole basin and its sub-basin were explored and quantified. The results showed that: 1) The average annual temperature and annual precipitation increase rate were 0.2 ℃/10a and 7.1 mm/10a during 1961 to 2021, respectively, with significant spatial and temporal distribution differences. 2) The first mode of the Empirical Orthogonal Function (EOF1) for both temperature and precipitation showed a consistent pattern, while EOF2 showed an opposite pattern. 3) In the TRB sub-basin, the difference between the highest and lowest annual average temperature increase rates was 0.1 ℃/10a. Similarly, the difference between the highest annual precipitation increase rates (in the Aksu River Basin) and lowest (in the Cherchen River Basin and Tarim River Mainstream Region) was 0.9 mm/10a. 4) The Kaidu River Basin had a significantly lower winter mean temperature of -9.69 ℃ compared to other sub-basins. Additionally, seasonal precipitation varied greatly among sub-basins, particularly in summer. 5) The annual mean temperature showed a strong positive correlation with the global mean temperature (coefficients over 0.5 for most sites), while the correlation for annual precipitation was weaker but still positive, ranging from 0.2 to 0.5. Significant positive correlations were observed for seasonal mean temperatures, especially in summer and autumn. Seasonal precipitation correlations were generally lower but had notable impacts in summer and autumn, particularly in sub-basins like Hotan River Basin and Aksu River Basin. Tarim River Basin precipitation temperature spatial and temporal heterogeneity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. INTRODUCTION In the past few decades, climate change and its impacts have attracted extensive attention from governments around the world (Tollefson J 2022 ; Abbass K et al. 2022 ). The IPCC Sixth Assessment Report clearly stated that global warming, due to greenhouse gas emissions from human activities, has increased by about 1.1°C since the period from1850 to 1900. It also predicted that warming will reach 1.5°C or more by the middle of the 21st century (Ipcc 2021 ). There was heterogeneity in the impact of warming on different regions (Xing Y et al. 2024 ; Byrne and Vitenu-Sackey 2024 ). Therefore, the characterization of climate change in each region in this context has increasingly become one of the prominent research topics in the field of climate science (Ipcc 2021 ). In recent years, a large number of studies have focused on the spatial and temporal distribution characteristics of regional temperature and precipitation in China (Zhang and Zhao 2022 ; Qin N et al. 2015 ; Wang Y et al 2024 ; Zhang and Liang 2020 ). For example, Zhang et al. (2022) found that between 1961 and 2010, the average annual precipitation in China had significant spatial differences and also fluctuated in different time periods. Zhao et al. ( 2021 ) found that regional temperature and precipitation in China generally showed uneven changes from 1960 to 2015. Guo et al. ( 2020 ) found that the variation of precipitation in China from 1961 to 2015 showed different trends in different regions and seasons. Liu et al. (2022) further found that precipitation in China increased by 13.9% between 1961 and 2018 as temperatures increased, and that this relationship varied significantly across regions. Northwest China is the largest Eurasian arid region and one of the most sensitive regions in terms of climate environment. It’s precipitation changes are of special significance to global and arid environment climate change (Yang X et al. 2023 ; Gao J et al. 2023 ). Since Academician Shi Yafeng proposed that since the 1980s, the climate in the arid area of Northwest China has gradually changed from “warm and dry” to “warm and wet”, and then experts and scholars have carried out a lot of research on the changes of precipitation and temperature in the arid area of Northwest China (Ma X et al. 2003 ; Shi Y et al. 2003 ). For example, Shang et al. ( 2018 ) revealed the Northwest China had tendency towards warming and humidification between 1961 and 2014. Zhang et al. ( 2021 ) found that from 1961 to 2018, the temperature and precipitation in the arid region of northwest China increased significantly, and the increasing trend of precipitation was particularly obvious. Wei et al. ( 2023 ) found that the temperature in the Qilian Mountains showed a significant upward trend from 1979 to 2018, and the temperature increase rate was significantly different in different seasons. Wang et al. ( 2023 ) used data from meteorological stations around the Badain Jaran Desert and observed an increasing trend in temperature and an insignificant trend in precipitation from 1960 to 2018. Based on data from meteorological stations, Zhang et al. ( 2023 ) analyzed changes in average temperature and precipitation at the Mogao Grottoes from 1990 to 2020, found that both showed an increasing trend. Tang ( 2021 ) found that both the average annual temperature and precipitation in Xinjiang exhibited an increasing trend from 1979 to 2018. Wang et al. ( 2020 ) found that in Xinjiang, precipitation changes exhibited marked asymmetry in both time and region between 1961 and 2019. Specifically, since 1985, annual total precipitation has significantly increased, particularly in the mountainous regions of western Xinjiang. As the largest inland river in China, Tarim River Basin (TRB) is abundant in natural resources and serves as the primary water source for southern Xinjiang. However, its ecological environment is fragile (Bai J et al. 2021 ; Chen Y et al. 2013 ; Wang Z et al. 2024 ). Hence, finding out the characteristics of climate change in TRB has a great impact on global/regional scale climate change research and socio-economic development (Zhu J et al. 2023 ; Milošević D et al. 2021 ). After a lot of research on “warming and humidification” in the northwest arid area, the TRB became the hot spot of climate research in the west arid area of China due to its distinctive regional characteristics (Yaning and Zongxue 2005 ; Xu J et al. 2009 ). For example, Xu et al. ( 2010 ) found that the annual average temperature in TRB increased significantly from 1960 to 2007, and the variation of precipitation had significant regional differences. Krysanova et al. ( 2015 ) found that from 1960 to 2000, the Aksu River Basin showed significant temperature rise and precipitation change. Zhang et al. ( 2020 ) found that the average annual temperature and average annual precipitation in the TRB generally increased from 1965 to 2015, but the precipitation did not increase significantly. Li et al. ( 2023 ) found that there was a clear trend of “warm and wet” in the TRB from 1961 to 2020. At present, numerous scholars have explored the multi-year changes in climate factors within the TRB, yielding extensive findings. However, most existing studies have focused on the temperature and precipitation changes in the entire TRB, with limited attention given to the variations in temperature and precipitation within its sub-basins. Additionally, these studies have predominantly employed traditional geographical statistical methods for their analyses (Chen Y et al. 2013 ; Li Y et al. 2024 ). The above studies showed that there has been an increasing trend in temperature and precipitation in the TRB over the past 60 years. However, the characteristics of spatial and temporal distribution of temperature and precipitation in the sub-basins of the TRB and their differences are still unclear. Therefore, this study analyzed the differences in the spatial and temporal characteristics of temperature and precipitation in the TRB and its sub-basins based on the observations of the past 60 years. In addition, explored and quantified the regional differences in the sub-basins of the TRB. The responses of temperature and precipitation to global changes in the whole basin and its sub-basins were investigated. Section 2 provides the basis for watershed zoning, and Section 3 provides data sources and processing methods. Section 4 provides the characteristics of spatial and temporal variations in temperature and precipitation in the TRB, along with the results of spatial and temporal modal analysis and the regional differences among its sub-basins. In addition, the relationship between temperature and precipitation changes in the entire TRB and its sub-basins with global temperature changes is also provided. The discussion and conclusion are given in Sections 5 and 6, respectively. 2. STUDY AREA The TRB (73°10′E to 94°05′E, 34°55′N to 43°08′N) is located in the southern part of Xinjiang, China (Fig. 1 ), and is the largest inland river basin in the world, with a total area of about 1.09×10 6 km 2 ( Zhang S et al. 2023 ; Wang Y et al. 2023 ). The topography of the region is complex, surrounded by the southern slopes of the Tianshan Mountains, the Kunlun Mountains, the Altun Mountains, and other highland mountains (Hou Y et al. 2022 ). Additionally, meteorological observation stations are unevenly distributed, particularly in deserts and alpine regions. The TRB is dry and windy, with a large difference in daily temperature, scarce precipitation, abundant light and heat resources, and strong evaporation (Xu Z X et al. 2004). In order to quantify the differences in temperature and precipitation among the sub-basins of the TRB, this study utilizes Xinjiang’s water resource zoning boundaries and the 1:250,000 administrative map to delineate distinct regions within the TRB. Specifically, the division includes the Kaidu River Basin (R1), Weigan River Basin (R2), Aksu River Basin (R3), Kashgar River Basin (R4), Yarkand River Basin (R5), Hotan River Basin (R6), Keriya River Basin (R7), Cherchen River Basin (R8) and Tarim River Mainstream Region (R9) (Table 1 ). Additionally, the study also delineates the Taklamakan Desert Region, the Kumtag Desert Region and the Western Qaidam Basin Desert Region (Fig. 1 ). However, due to limitations in natural conditions and observational data, these areas were not included in the analysis. Table 1 Watershed zoning and distribution of meteorological stations Primary Zone TRB secondary Zone R1 R2 R3 R4 R5 R6 R7 R8 R9 Number of sites Temperatures 6 5 6 8 6 4 3 2 2 Precipitation 5 5 6 7 6 4 3 2 2 3. DATA AND METHODS 3.1. Data sources In this study, monthly temperature and precipitation data from 42 national meteorological stations in Xinjiang region from 1961 to 2021, as well as surface air temperature data, were used. Among them, the surface air temperature data were obtained from the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies (GISS) ( https://data.giss.nasa.gov/gistemp/ ), while the monthly temperature and precipitation data were provided by the National Meteorological Information Center of China Meteorological Administration ( http://www.nmic.cn/ ). To ensure long-term continuity and representativeness, each station required at least 52 years of observations with a minimum of 8 months of data per year. Observations with precipitation less than 0.1 mm were excluded to ensure data accuracy. After eliminating missing values and temporal inhomogeneities, data from 42 stations for temperature and 40 stations for precipitation from 1961 to 2021 were selected for analysis (Table 1 ). Seasonal scales were assessed based on the traditional division of our weather service, which divides the four seasons into MAM (March to May), JJA (June to August), SON (September to November), and DJF (December to February). 3.2. Methods In this study, we analyzed the temporal changes in temperature and precipitation in the TRB from 1961 to 2021 using linear trend analysis (Worku M A et al. 2022). We applied the Empirical Orthogonal Function (EOF) method to detect the temporal and spatial patterns of the TRB and performed significance testing on the spatial modes using North’s method (Xia Z et al. 2023 ). Additionally, we used the Mann-Kendall (M-K) mutation test to identify abrupt changes and trends in temperature and precipitation (Alemu and Dioha 2020 ). Furthermore, we calculated the correlation coefficients between the temperature and precipitation of the entire TRB and its sub-basins with the global mean temperature (GMT) to explore the response of the TRB’s temperature and precipitation to global warming (Aihaiti A et al. 2023 ). 4. RESULTS 4.1 TRB temporal and spatial trends in temperature and precipitation In the past 60 years, the average temperature of the TRB was 10.1 ℃, the highest average annual temperature was 11.3 ℃ in 2016, and the lowest was 8.9 ℃ in 1967. The difference between the highest and lowest average annual temperatures was 2.4 ℃, indicating significant variability in inter-annual temperatures. In the past 60 years, the average annual temperature of the TRB has shown a significant upward trend, with an increase rate of 0.2 ℃/10a (Fig. 2 a). From 1961 to 2021, the TRB average annual precipitation was 74.2 mm. The maximum value of the average annual precipitation was 153.6 mm in 2010, and the minimum value of the average annual precipitation was 30.5 mm in 1985, with an extreme value ratio of 5.04 (Fig. 2 b). The average annual precipitation has exhibited a general increasing trend, with a growth rate of 7.1 mm/10a. The spatial distribution of average annual temperature and annual precipitation was presented in Fig. 2 . The average annual temperature of the TRB in the past 60 years was above 8 ℃. Among them, stations around the Tarim Basin Desert Area recorded average annual temperatures of 10 and 16°C. Meanwhile, stations in the northern and western fringes registered temperatures of 0 to 6°C during the same period (Fig. 2 c). That is, the average annual temperature of the TRB was closely related to the topography, which was higher around the Tarim Basin Desert Area than in other regions. Over the last 60 years, the annual precipitation at most stations throughout the TRB was above 30 mm. The annual precipitation for the last 60 years at the stations in the northwestern fringe of the region were above 90 mm, while at the stations in the southeastern region was between 20 and 50 mm (Fig. 2 d). That is, the annual precipitation showed a gradual decrease from northwest to southeast, with the northwest region higher than the southeast region in terms of spatial distribution. 4.2 TRB spatial and temporal modes of temperature and precipitation In order to further study the spatial and temporal distribution characteristics of meteorological elements in the TRB, EOF analysis was conducted on the average annual temperature and annual precipitation data, followed by North’s significance test. This process identified the principal EOFs and their corresponding PCs in the TRB. The values and contributions of the first to fourth eigenvectors of average annual temperature and average annual precipitation for the TRB from 1961 to 2021 were shown in Table 2 . The cumulative variance contribution of the first and second modes of average annual temperature amounted to 76.87% (significant at 0.05 level). For annual precipitation, it was 56.98% (significant at the 0.05 level). This indicated that the first and second eigenvalues could better reflect the type of spatial distribution of average annual temperature and annual precipitation in the TRB in the past 60 years, as shown in Fig. 3 . Table 2 Variance contributions and accumulated variance contribution of the first to four eigenvectors of the EOF decomposition of average annual temperature and annual precipitation in the TRB from 1961 to 2021. Serial number EOF Variance contribution rate (%) Cumulative variance contribution rate (%) Temperature Precipitation Temperature Precipitation Temperature Precipitation 1 27.55 17.68 69.49 46.99 69.49 46.99 2 2.93 3.76 7.38 9.99 76.87 56.98 3 2.16 2.03 5.45 5.39 82.31 62.37 4 1.48 1.62 3.73 4.29 86.04 66.66 The eigenvector values of all stations in mode 1 of annual mean temperature were positive, indicating a highly consistent temperature change trend in the TRB from 1961 to 2021. That is, the temperature distribution characteristics of the entire basin were either entirely in high temperature or entirely in low temperature (Fig. 3 a). The eigenvector value starts from the southern part of the TRB and gradually increases to the north. The high value center was located in the Kaidu River Basin, the Weigan River Basin and the northern part of the Yerqiang River Basin, indicating that this region had the highest average annual temperature and was the sensitive center of temperature change. In contrast, the low-value center was in the southern Yerqiang River Basin, southern Hetian River Basin, and Keriya River Basin. The variance contribution rate of mode 2 was 7.38%, which was also a typical spatial distribution of temperature in TRB. The Yerqiang River Basin, Hotan River Basin, western Chelchen River Basin, and Keriya River Basin showed reverse change characteristics compared to the Kaidu River Basin, Weigan River Basin, Aksu River Basin, eastern Chelchen River Basin, and Kashgar River Basin. The magnitude decreases gradually from south to north, indicating that the change of temperature in the basin decreases gradually from south to north. The high value center was in the southern part of TRB, and the low value center was in the northern part of TRB, showing a north-south reverse distribution pattern. That is, higher temperatures in the south corresponded to lower temperatures in the north, and vice versa. The time coefficient can reflect the time change characteristic corresponding to the spatial distribution mode of the eigenvector. A positive time coefficient indicates a change direction consistent with the spatial mode, while a negative value indicates the opposite. The greater the absolute value of the time coefficient, the greater the typical degree of the mode (Haynes and Beare 1997 ). The spatial distribution characteristics of average annual temperature in TRB were classified into four main types: high temperature in the whole basin, low temperature in the whole basin, high temperature in the south and low temperature in the north, and low temperature in the south and high temperature in the north (Fig. 4 ). Figure 4 showed that the trend slope of the time coefficient for mode 1 was greater than zero, indicating to some extent that the annual average temperature of the basin has increased in the past 60 years. that was, mode 1 had a trend of high temperature of the whole basin. For mode 2, a positive time coefficient represents high temperature in the south and low temperature in the north, while a negative coefficient represents the reverse. The trend slope of time coefficient of mode 2 of annual rainfall in TRB was greater than zero, indicating that high temperature in the south and low temperature in the north were the main distribution types of the basin. The eigenvector values of all stations in mode 1 of annual precipitation were positive, indicating a consistent variation trend in TRB’s precipitation from 1961 to 2021, with the entire basin being either rainy or less rainy (Fig. 3 c). The eigenvector value starts from the western part of the basin and gradually increases to the east. The high value center was located in the western part of the Kaidu River Basin and the Cherchen River Basin, indicating that this region had the highest annual precipitation and was the sensitive center of precipitation change, while the low value center was located in the Yerqiang River Basin and the Kashgar River Basin. The variance contribution rate of mode 2 was 9.99%, which was also a typical spatial distribution form of precipitation in TRB. The Kashgar River Basin and Yerqiang River Basin showed opposite characteristics compared to the Kaidu River Basin, Weigan River Basin, and Aksu River Basin. The magnitude decreases gradually from west to east, indicating that the change of precipitation in the basin decreases gradually from west to east. The east-west distribution pattern was reversed, that was, higher precipitation in the west corresponded to lower precipitation in the east, and vice versa. Figure 4 c shows that the trend slope of the time coefficient of mode 1 was greater than zero, indicating to a certain extent that the annual precipitation of the basin had an increasing trend in the past 60 years, that was, mode 1 had a tendency of rainfall in the whole basin. However, when the time coefficient of mode 2 was positive, the precipitation distribution was rainy in the west and rainy in the east; when the time coefficient was negative, it was rainy in the north and rainy in the west. The trend slope of the time coefficient of mode 2 of annual rainfall in TRB was greater than zero, which indicates that the main distribution type of the annual rainfall in the west was more rainy than that in the east. 4.3 Differences of temperature and precipitation in the sub-basins 4.3.1 Differences of temporal trends From 1961 to 2021, the average annual temperatures in R1 to R9 were 7.1 ℃, 10.6 ℃, 9.9 ℃, 9.6 ℃, 10.6 ℃, 12.2 ℃, 12.1 ℃, 11.3 ℃, and 11.0 ℃, respectively. There were differences in the occurrence time of the lowest and highest annual mean temperature in each sub-basins of TRB. Specifically, the lowest annual average temperature of R2 and R5 appeared in 1976 and 1969, at 9.4 ℃ and 8.1 ℃, respectively. The lowest annual mean temperature of R3 and R4 occurred in 1974, at 8.7 ℃ and 8.1 ℃ respectively. The lowest annual mean temperature of R1, R6, R7, R8 and R9 were all occurred in 1967, at 5.7 ℃, 10.9 ℃, 10.5 ℃, 9.6 ℃ and 9.8 ℃ respectively. On the other hand, the highest annual average temperature of R1, R2, R4 and R6 occurred in 2007, which were 8.4 ℃, 11.7 ℃, 10.8 ℃ and 13.6 ℃ respectively. The highest annual average temperature values of R3, R5, R7, R8 and R9 all occurred in 2016, which were 11.0 ℃, 12.2 ℃, 13.6 ℃, 12.6 ℃ and 12.4 ℃ respectively. Furthermore, the region with the largest difference between the highest and lowest annual mean temperatures was R5 (4.1 ℃), while the smallest difference was in R2 (2.3 ℃), indicating significant inter-annual temperature variations in each sub-basin. The linear trend of average annual temperature in each sub-basin showed a significant increase from 1961 to 2021 (Fig. 5 ). The average annual temperature increase rates in R1, R2, R3, R4, R8, and R9 were all 0.2 ℃/10a, while those in R5, R6, and R7 were 0.3 ℃/10a (Table 3 ). The difference between the highest and lowest rates of increase in average annual temperature was 0.1 ℃/10a. In addition, there were differences in the time of abrupt change of annual mean temperature in each branch basin. Compared with other regions, R5, R6, R7 and R9 had slightly later mean temperature change time, which was later than 2000 (Table 3 ). Table 3 The difference of climate tendency rate and abrupt change time of annual mean temperature and annual precipitation in each branch basin of TRB from 1961 to 2021. secondary Zone Climatic tendency rate (℃/10a、mm/10a) Mutation time(year) Temperature Precipitation temperature precipitation R1 0.2 4.7 1994 1988, 2009, 2010, 2011, 2012, 2018,2021 R2 0.2 6.0 1991 1978, 2020, 2021 R3 0.2 12.9 1999 1995 R4 0.2 8.8 1998 2003, 2006, 2008 R5 0.3 6.8 2004 2010, 2019, 2021 R6 0.3 7.4 2003 2010, 2011, 2012, 2013, 2016, 2019, 2020 R7 0.3 4.1 2001 2010, 2011, 2012, 2020, 2021 R8 0.2 1.9 1993 1974, 1975, 1981, 1983, 1984, 1985, 1987, 2020, 2021 R9 0.2 2.5 2002 2008, 2009, 2010, 2011, 2012, 2020, 2021 From 1961 to 2021, the annual precipitation in R1 to R9 were 95.0 mm, 75.9 mm, 109.3 mm, 91.8 mm, 61.0 mm, 44.5 mm, 43.6 mm, 27.0 mm, and 42.7 mm, respectively. There were differences in the occurrence time of the lowest and highest annual precipitation in each sub-basins of TRB. Specifically, the lowest annual precipitation of R1, R3 and R4 appeared in 1985, respectively, which were 32.6 mm, 41.9mm and 24.9 mm. The lowest annual precipitation in R2 was 30.6 mm in 1965. The lowest annual precipitation in R2 was 30.6 mm in 1965. The lowest annual precipitation in R5 was 22.9 mm in 1978. The lowest annual precipitation in R6 was 6.9 mm in 1963. The lowest annual precipitation in R7 was 8 mm in 1986. The lowest annual precipitation of R8 and R9 occurred in 2001, 6.1 mm and 12.4 mm respectively. On the other hand, the highest annual precipitation in R1 was 173.5 mm in 2016. The highest annual precipitation in R2 was 159.8 mm in 1987. The highest annual precipitation of R3, R4, R5, R6 and R7 occurred in 2010, which were 225.4 mm, 230.4 mm, 146.6 mm, 138.6 mm and 153.1 mm respectively. The highest annual precipitation in R8 was 80.2 mm in 2005. The highest annual precipitation in R9 was 96.9 mm in 2015. The linear trend of annual precipitation in each sub-basin during 1961–2021 showed an obvious increasing trend (Fig. 5 b). Compared with the annual mean temperature, the increase rate of annual precipitation in each sub-watershed was significantly different. The annual precipitation increase rates in regions R1 to R9 were 4.7 mm/10a, 6.0 mm/10a, 12.9 mm/10a, 8.8 mm/10a, 6.8 mm/10a, 7.4 mm/10a, 4.1 mm/10a, 1.9 mm/10a and 2.5 mm/10a. There was a difference of 11mm/10a between the region R3 with the largest annual precipitation growth rate and the region R8 with the smallest precipitation growth rate. In addition, compared with the annual average temperature, except for region 3, the annual precipitation of the other sub-basins had more abrupt points, which had obvious interannual variation (Table 3 ). 4.3.2 Differences in spatial distribution During the past 60 years, the average annual temperature and annual precipitation in the TRB have shown significant spatial differentiation. When analyzed on an interannual scale, most of the average annual temperatures in the TRB were above 8°C. Among them, the average annual temperature in R1 to R4 lower than that in R5 to R9. On the other hand, the annual precipitation of R1 to R5 was higher than of R6 to R9. From 1961 to 2021, the seasonal mean temperature of each branch basin showed the mean temperature in summer > mean temperature in spring > mean temperature in autumn > mean temperature in winter, but there were differences in each season. The average spring temperatures of R2, R6 and R7 were 13.91 ℃, 15.39 ℃ and 15.35 ℃, respectively, which were significantly higher than other regions in the same season. The mean summer temperatures of R6, R7, R8 and R9 were 24.35 ℃, 24.41 ℃, 25.41 ℃ and 25.07 ℃, respectively, which were significantly higher than other regions in the same season. The temperature of R6 and R7 in autumn was higher, 11.79 ℃ and 11.41 ℃ respectively, which was significantly higher than that of other regions in the same season. The average winter temperature of R6 and R7 was higher, -2.59 ℃ and − 2.83 ℃ respectively, which was significantly higher than that of other regions in the same season (Fig. 6 e). Among them, the average temperature of R1 in all seasons was significantly lower than that of other sub-basins, mainly because R1 contains an extreme low temperature point (Fig. 6 a-d). From 1961 to 2021, the seasonal precipitation of each branch watershed showed the following pattern: summer precipitation > spring precipitation > autumn precipitation > winter precipitation, but there were differences in each season. Among them, the spring precipitation of R3 and R4 was 23.95 mm and 26.11 mm, respectively, which was significantly higher than other regions in the same season. The lowest spring precipitation was 4.51 mm in R8. The summer precipitation of R1 and R3 was 58.37 mm and 60.35 mm, respectively, which was significantly higher than that of other regions in the same season. R8 has the lowest summer precipitation at 18.93 mm. The autumn precipitation of R3 and R4 was 19.21 mm and 16.16 mm, respectively, which was significantly higher than other regions in the same season. R8 has the lowest precipitation in autumn at 1.57 mm. The winter precipitation of R4 was 10.07 mm, which was significantly higher than that of other regions in the same season (Fig. 6 i). R9 had the lowest winter precipitation of 1.57 mm (Fig. 6 j). 4.4 Possible impacts of global warming on the TRB Through the above analysis, it was found that the temperature and precipitation in this basin both showed an increasing trend, and the trend was different in each branch basin. We thus analyzed the connection between this trend and global warming to identify if and where increasing global average temperatures were affecting temperature and precipitation in the TRB. Figure 7 a showed the relationship between the annual average temperature of TRB and the global average temperature of land and ocean, with a correlation coefficient ranging from − 0.1 to 0.8. Notably, 97.6% of the sites exhibited a positive correlation, and 71.4% of the sites had a correlation coefficient greater than 0.5. This indicated that the annual mean temperature changes in most regions of the TRB were consistent with the global mean temperature changes, and there was little difference in response to global warming among the sub-basins. Figure 7 b illustrated the relationship between annual precipitation of TRB and global average temperature of land and ocean. Compared to temperature, the response of precipitation to global warming was slightly lower, with correlation coefficients ranging from 0.1 to 0.6. All stations in the region showed a positive correlation between annual precipitation and global mean temperature. However, only 10% of the sites had a correlation coefficient greater than 0.5, while 82.5% had coefficients between 0.2 and 0.5. This suggested that while the annual precipitation trend aligns with global temperature changes, the relationship was weak. Compared with the average annual temperature, the correlation between annual precipitation and global warming was significantly different among the sub-basins, and the correlation between annual precipitation in R3 and global warming was higher than that in other sub-basins. To gain a more comprehensive understanding of the impact of global warming on temperature and precipitation changes in the TRB, we further explored the relationship between seasonal mean temperatures and seasonal precipitation with the global mean temperature. This analysis provided a scientific basis for developing effective climate change adaptation strategies for the region. The seasonal mean temperature in spring and winter in the TRB was positively correlated with the global mean temperature, with correlation coefficients ranging from 0 to 0.7 and 0.1 to 0.6, respectively. The summer mean temperature at 86% of the sites and the autumn mean temperature at 88% of the sites were positively correlated with global mean temperature changes, indicating a significant impact of global warming on temperature variation in the TRB, especially in summer and autumn. In addition, the correlation between seasonal temperature variations and global mean temperature varied across different sub-basins (Fig. 8 i). For example, the correlation coefficient between summer mean temperature and global mean temperature in each branch basin was quite different, and the difference between R8 with the highest correlation and R4 with the lowest correlation was 0.5. The correlation coefficient between autumn mean temperature and global mean temperature was strong, especially R5 (0.6) and R7 (0.7). The correlation coefficients between seasonal precipitation and global mean temperature in spring, summer, autumn and winter in TRB were 0 to 0.4, -0.1 to 0.5, -0.1 to 0.42 and − 0.1 to 0.3, respectively. The proportion of sites with positive correlations between seasonal precipitation and global mean temperature was 95% in spring, 90% in summer, 98% in autumn, and 87.5% in winter. Although the correlation between seasonal precipitation and global mean temperature in this basin was lower than that between seasonal mean temperature and global mean temperature, global warming still affects the seasonal precipitation in TRB, especially in summer and autumn. In addition, the correlation between seasonal precipitation variations and global mean temperature also varied among different sub-basins (Fig. 8 j). For instance, the correlation coefficients between spring precipitation and global mean temperature ranged from 0.1 to 0.3. The correlation coefficients between summer precipitation and global mean temperature vary greatly among the sub-basins, among which R6 had the highest correlation with global mean temperature (0.43), and R8 had the lowest correlation with global mean temperature (0.04), with a difference of 0.39. There was a strong positive correlation between precipitation and global mean temperature change in most basins in autumn, especially R3 (0.4). The correlation between winter mean temperature and global mean temperature was the highest in R8 (0.2), while other sub-basins showed lower correlations. 5. DISCUSSION Studies have shown that the rate of temperature increase in the TRB has been significant since the mid-1980s, with an increase of 0.2°C/10a. About 72.3% of the meteorological stations experienced a significant increase in precipitation, with an average rate of increase of 7.5 mm/10a (Li W et al. 2023 ). This is the same as the overall trend of average of temperature and average of precipitation in the TRB obtained in this study. In particular, the growth rate of average annual temperature was slightly higher than the 0.2°C/10a derived in this study, and the growth rate of average annual precipitation was slightly higher than the 7.1 mm/10a derived in this study. This may be mainly because the two studies were on different time scales. At the same time, most of the previous studies focused on the whole region, and there were relatively few studies on the climatic elements of each branch basin. In addition, few scholars have applied the EOF method to the analysis of the spatio-temporal variation characteristics of temperature and precipitation in this basin. In this study, the temporal and spatial variations of average temperature and precipitation were analyzed using EOF. Based on the long time series observed data, the whole region was divided into secondary watersheds, such as R1 to R9. Furthermore, quantitatively analyzed the differences in the changes of their meteorological elements. Compared with the average annual temperature, the annual precipitation growth rate of each branch basin was different. There were also differences in the abrupt change time of average annual mild precipitation in each branch basin. This was at variance with the results of Zhang et al. ( 2020 ), which showed that the abrupt change in precipitation over the whole region was concentrated in 1986. The main reasons for these differences may be that the time scales, regional division and the mutation tests methods of the studies are different. This study used the M-K mutation test, while Zhang et al. ( 2020 ) used the sliding mean difference test. In order to tap more accurate and effective climate change signals in the TRB, the applicability of different methods in the TRB and the reliability of the results need to be further explored. In this study, the climate station equipment in the middle TRB, where the Taklimakan Desert is located, is easily affected by extreme weather, resulting in the absence or incomplete climate station data in this region, which failed to analyze the temperature and precipitation changes in this region in detail. In the future, reanalysis data sets or remote sensing data sets can be used for further research to make up for the deficiency of ground climate station data. In summary, over the last 60 years, the TRB and its sub-basins have experienced a tendency towards warmth and humidity, but with regional differences. The rising temperatures and humidity in the basin provide more favorable conditions for extreme precipitation events (Li W et al. 2023 ). To better cope with climate-related disasters, it is necessary to strengthen meteorological monitoring and numerical early warning for the basin and its sub-basins. Additionally, this study only analyzed the relationship between temperature, precipitation and global warming in this basin and its sub-basins. Future studies should further explore the regional impacts of climate change, changes in atmospheric circulation patterns, and the effects of human activities on temperature and precipitation in the TRB. 6. CONCLUSIONS The study utilized data from meteorological stations in the TRB from 1961 to 2021, and employed linear regression method, EOF and M-K mutation test for analysis. Firstly, the spatial and temporal distribution characteristics of average annual temperature and average annual precipitation in the TRB were analyzed. Secondly, the EOF was used to analyze the characteristics of TRB temporal and spatial modal changes. Thirdly, the differences in the spatial and temporal characteristics of meteorological elements in the whole basin and sub-basins of the TRB were quantitatively analyzed. Finally, the response of average temperature and precipitation to global warming in the whole basin and sub-basins of the TRB was quantitatively analyzed. The results showed that: The average annual temperature and average annual precipitation of the TRB have shown an overall increasing trend over the past 60 years. The rate of increase was 0.2°C/10a and 7.1 mm/10a, respectively. Compared with the average annual temperature growth rate, the annual precipitation growth rate of each branch basin was quite different, and the difference between the region with the highest growth rate and the region with the lowest growth rate was 11.0 mm/10a. The high values of average annual temperature were mainly distributed in the western and southern stations of the TRB (10 ~ 16°C), and the high values of annual precipitation were mainly distributed in the western and northern stations (50 ~ 90 mm). The first mode of the Empirical Orthogonal Function (EOF1) for both the average annual temperature and annual precipitation showed a consistent pattern, while EOF2 an opposite pattern. Combining the time coefficients of their modes yielded a significant increasing trend in average annual temperature and average annual precipitation for the TRB from 1961 to 2021. The average annual temperature and annual precipitation in all sub-basins of the TRB have shown a significant increasing trend over the past 60 years, but there were regional differences. Among them, R5, R6 and R7 have higher warming rates, and the annual average precipitation increase rate and change range of each sub-basin were more significant than the temperature change, especially the annual average precipitation increase rate of R3 sub-basin was the largest. In addition, except R3, the average annual precipitation of other sub-basins had obvious inter-annual fluctuation. There were obvious differences of temperature and precipitation in each branch basin during the season. In terms of temperature, the mean temperature in summer was generally higher, and R6, R7, R8 and R9 have the highest mean temperature in summer. In terms of spring mean temperature, R2, R6 and R7 were significantly higher than other regions. In terms of average temperature in autumn and winter, R6 and R7 also show higher values. In terms of precipitation, summer precipitation is generally large, especially R1 and R3; Spring precipitation is highest in R3 and R4. The precipitation in autumn is more evenly distributed, but R3 and R4 are still higher than other regions. In terms of winter precipitation, R4 has the highest precipitation, while R9 has the lowest. These results reflect the significant temporal and spatial variations of seasonal temperature and precipitation in different regions of TRB. A positive correlation was observed between the annual mean temperature of the TRB and global mean temperature changes, with most sites exhibiting a correlation coefficient greater than 0.5. In contrast, the correlation between annual precipitation and global mean temperature was weaker but still positive, with most sites having coefficients between 0.2 and 0.5. Seasonal mean temperatures in spring and winter showed significant positive correlations with global mean temperature, particularly in summer and autumn. Seasonal precipitation correlations were generally lower, but summer and autumn had more substantial impacts, especially in sub-basins like R6 and R3. Declarations Conflicts of Interest: The authors declare no conflict of interest. Funding: This research was funded by The Third Xinjiang Scientific Expedition, grant number 2022xjkk030502; Science and Technology Innovation Team (Tianshan Innovation Team) Project, grant number 2022TSYCTD0007; Innovation Team Project of Xinjiang Meteorological Service, grant number ZD202306. Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and data curation were performed by Meiqi Song, Cong Wen, Yisilamu Wulayin, Jian Peng and Jiacheng Gao. The methodology was performed by Wen Huo, Fan Yang, Chenglong Zhou and Yu Wang. The investigation, visualization, writing - original draft preparation and analysis were performed by Siqi Wang, Ailiyaer Aihaiti, Yongqiang Liu, Hajigul Sayit and Mamtimin Ali. And all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data Availability Statement: The datasets used in this study can be provide by Mamtimin Ali ( [email protected] ) upon request. References Tollefson J (2022) Climate change is hitting the planet faster than scientists originally thought[J]. Nature Abbass K, Qasim MZ, Song H et al (2022) A review of the global climate change impacts, adaptation, and sustainable mitigation measures[J]. Environ Sci Pollut Res Int 29(28):42539–42559 Ipcc (2021) Climate Change 2021: The Physical Science Basis. 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Water Power 46(04):28–34 (In Chinese) Li W, Zhu C, Chen Y (2023) Recent changes in the water and ecological condition at the arid Tarim River Basin[J] Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4717419","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":333482561,"identity":"35b7ead8-68c5-4832-9c72-c8e2fcb4215b","order_by":0,"name":"Siqi Wang","email":"","orcid":"","institution":"Institute of Desert Meteorology, China Meteorological Administration","correspondingAuthor":false,"prefix":"","firstName":"Siqi","middleName":"","lastName":"Wang","suffix":""},{"id":333482562,"identity":"ca0cfafa-a8eb-4835-91a1-025e6261287a","order_by":1,"name":"Aihaiti Ailiyaer","email":"","orcid":"","institution":"Institute of Desert 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shaded color indicates the elevation of the TRB (m), different color symbols represent the distribution of stations in the sub-basins of the TRB.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4717419/v1/bfe2d8ca5f75e1946892daf4.jpeg"},{"id":61779468,"identity":"7ce95689-93f1-413a-a8a2-3fd012dcc5c6","added_by":"auto","created_at":"2024-08-05 13:21:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23807,"visible":true,"origin":"","legend":"\u003cp\u003e(a, b) Linear trends of average annual temperature (℃) and average annual precipitation (mm) in the TRB from 1961 to 2021; (c, d) Spatial distribution of average annual temperature and annual precipitation in the TRB from 1961 to 2021.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4717419/v1/2739585891619e636e3913e2.png"},{"id":61778106,"identity":"8515b926-510c-4253-a96a-6321d75d1921","added_by":"auto","created_at":"2024-08-05 13:05:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":21431,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of the first eigenvector (a, c) and second eigenvector (b, d) of the EOF analysis of the average annual temperature and annual precipitation in the TRB from 1961to 2021.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4717419/v1/3edca6e3a2a5bd328b4b2ecc.png"},{"id":61778109,"identity":"a35adf4b-a17b-440e-b3e0-00cb25fd5566","added_by":"auto","created_at":"2024-08-05 13:05:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":21553,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal trends of EOF1 (a, c) and EOF2 (b, d) for average annual temperature and average annual precipitation in the TRB from 1961 to 2021. Where the solid line is the PC value and the dashed line is the linearly fitted trend.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4717419/v1/865527a877085f9eac68f4fb.png"},{"id":61778667,"identity":"6dac98af-e3fa-411e-98e6-4cf1c917afea","added_by":"auto","created_at":"2024-08-05 13:13:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":38699,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in average annual temperature and annual precipitation in the sub-basins of the TRB from 1961 to 2021.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4717419/v1/e6331479540b66502a978902.png"},{"id":61778111,"identity":"fd933960-012d-44dd-af5a-1dc45c79bf9c","added_by":"auto","created_at":"2024-08-05 13:05:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":96045,"visible":true,"origin":"","legend":"\u003cp\u003e(a-d) shows the spatial distribution of average seasonal temperature (°C) in the sub-basins from 1961 to 2021; (f-i) the spatial distribution of seasonal precipitation (mm) in the sub-basins from 1961 to 2021. (e) and (j) shows the thermal maps of average seasonal temperature (℃) and seasonal precipitation (mm) in the sub-basins.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4717419/v1/8e661d44d7897c15ba1f83f7.png"},{"id":61778114,"identity":"b468fa0b-cf68-470f-935a-fcc9bf793bfe","added_by":"auto","created_at":"2024-08-05 13:05:24","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":21217,"visible":true,"origin":"","legend":"\u003cp\u003e(a-b) shows the relationship between annual mean temperature (°C) and annual precipitation (mm) and global warming in the TRB. The bar chart shows the relationship between annual mean temperature (°C) and annual precipitation (mm) in each sub-basin and global warming.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4717419/v1/e4b6c9e8e3da897e24bdc798.png"},{"id":61778112,"identity":"fb28dec7-15fa-4f9a-bebe-8e0e982db94c","added_by":"auto","created_at":"2024-08-05 13:05:24","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":121547,"visible":true,"origin":"","legend":"\u003cp\u003e(a-h) shows the relationship between seasonal mean temperature (°C) and seasonal precipitation (mm) and global warming in the TRB. (i-h) shows the relationship between seasonal mean temperature (°C) and seasonal precipitation (mm) in each sub-basin and global warming.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4717419/v1/e4c124fa4585f16f1adce7e3.png"},{"id":63546693,"identity":"e66fc3f5-1ea6-49dd-a685-f7143be2c243","added_by":"auto","created_at":"2024-08-29 11:30:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1291766,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4717419/v1/30c395f7-d73c-4356-b570-9e390e6d21fa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The increase of temperature and precipitation in the different regions of Tarim River Basin has spatial and temporal heterogeneity over 1961-2021.","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eIn the past few decades, climate change and its impacts have attracted extensive attention from governments around the world (Tollefson J \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Abbass K et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The IPCC Sixth Assessment Report clearly stated that global warming, due to greenhouse gas emissions from human activities, has increased by about 1.1\u0026deg;C since the period from1850 to 1900. It also predicted that warming will reach 1.5\u0026deg;C or more by the middle of the 21st century (Ipcc \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). There was heterogeneity in the impact of warming on different regions (Xing Y et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Byrne and Vitenu-Sackey \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, the characterization of climate change in each region in this context has increasingly become one of the prominent research topics in the field of climate science (Ipcc \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, a large number of studies have focused on the spatial and temporal distribution characteristics of regional temperature and precipitation in China (Zhang and Zhao \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Qin N et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wang Y et al \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang and Liang \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For example, Zhang et al. (2022) found that between 1961 and 2010, the average annual precipitation in China had significant spatial differences and also fluctuated in different time periods. Zhao et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that regional temperature and precipitation in China generally showed uneven changes from 1960 to 2015. Guo et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that the variation of precipitation in China from 1961 to 2015 showed different trends in different regions and seasons. Liu et al. (2022) further found that precipitation in China increased by 13.9% between 1961 and 2018 as temperatures increased, and that this relationship varied significantly across regions. Northwest China is the largest Eurasian arid region and one of the most sensitive regions in terms of climate environment. It\u0026rsquo;s precipitation changes are of special significance to global and arid environment climate change (Yang X et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gao J et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Since Academician Shi Yafeng proposed that since the 1980s, the climate in the arid area of Northwest China has gradually changed from \u0026ldquo;warm and dry\u0026rdquo; to \u0026ldquo;warm and wet\u0026rdquo;, and then experts and scholars have carried out a lot of research on the changes of precipitation and temperature in the arid area of Northwest China (Ma X et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Shi Y et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). For example, Shang et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) revealed the Northwest China had tendency towards warming and humidification between 1961 and 2014. Zhang et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that from 1961 to 2018, the temperature and precipitation in the arid region of northwest China increased significantly, and the increasing trend of precipitation was particularly obvious. Wei et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that the temperature in the Qilian Mountains showed a significant upward trend from 1979 to 2018, and the temperature increase rate was significantly different in different seasons. Wang et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) used data from meteorological stations around the Badain Jaran Desert and observed an increasing trend in temperature and an insignificant trend in precipitation from 1960 to 2018. Based on data from meteorological stations, Zhang et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) analyzed changes in average temperature and precipitation at the Mogao Grottoes from 1990 to 2020, found that both showed an increasing trend. Tang (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that both the average annual temperature and precipitation in Xinjiang exhibited an increasing trend from 1979 to 2018. Wang et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that in Xinjiang, precipitation changes exhibited marked asymmetry in both time and region between 1961 and 2019. Specifically, since 1985, annual total precipitation has significantly increased, particularly in the mountainous regions of western Xinjiang.\u003c/p\u003e \u003cp\u003eAs the largest inland river in China, Tarim River Basin (TRB) is abundant in natural resources and serves as the primary water source for southern Xinjiang. However, its ecological environment is fragile (Bai J et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chen Y et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wang Z et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Hence, finding out the characteristics of climate change in TRB has a great impact on global/regional scale climate change research and socio-economic development (Zhu J et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Milošević D et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). After a lot of research on \u0026ldquo;warming and humidification\u0026rdquo; in the northwest arid area, the TRB became the hot spot of climate research in the west arid area of China due to its distinctive regional characteristics (Yaning and Zongxue \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Xu J et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For example, Xu et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) found that the annual average temperature in TRB increased significantly from 1960 to 2007, and the variation of precipitation had significant regional differences. Krysanova et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found that from 1960 to 2000, the Aksu River Basin showed significant temperature rise and precipitation change. Zhang et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that the average annual temperature and average annual precipitation in the TRB generally increased from 1965 to 2015, but the precipitation did not increase significantly. Li et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that there was a clear trend of \u0026ldquo;warm and wet\u0026rdquo; in the TRB from 1961 to 2020. At present, numerous scholars have explored the multi-year changes in climate factors within the TRB, yielding extensive findings. However, most existing studies have focused on the temperature and precipitation changes in the entire TRB, with limited attention given to the variations in temperature and precipitation within its sub-basins. Additionally, these studies have predominantly employed traditional geographical statistical methods for their analyses (Chen Y et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Li Y et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe above studies showed that there has been an increasing trend in temperature and precipitation in the TRB over the past 60 years. However, the characteristics of spatial and temporal distribution of temperature and precipitation in the sub-basins of the TRB and their differences are still unclear. Therefore, this study analyzed the differences in the spatial and temporal characteristics of temperature and precipitation in the TRB and its sub-basins based on the observations of the past 60 years. In addition, explored and quantified the regional differences in the sub-basins of the TRB. The responses of temperature and precipitation to global changes in the whole basin and its sub-basins were investigated. Section 2 provides the basis for watershed zoning, and Section 3 provides data sources and processing methods. Section 4 provides the characteristics of spatial and temporal variations in temperature and precipitation in the TRB, along with the results of spatial and temporal modal analysis and the regional differences among its sub-basins. In addition, the relationship between temperature and precipitation changes in the entire TRB and its sub-basins with global temperature changes is also provided. The discussion and conclusion are given in Sections 5 and 6, respectively.\u003c/p\u003e"},{"header":"2. STUDY AREA","content":"\u003cp\u003eThe TRB (73\u0026deg;10\u0026prime;E to 94\u0026deg;05\u0026prime;E, 34\u0026deg;55\u0026prime;N to 43\u0026deg;08\u0026prime;N) is located in the southern part of Xinjiang, China (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and is the largest inland river basin in the world, with a total area of about 1.09\u0026times;10\u003csup\u003e6\u003c/sup\u003e km\u003csup\u003e2 (\u003c/sup\u003eZhang S et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang Y et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The topography of the region is complex, surrounded by the southern slopes of the Tianshan Mountains, the Kunlun Mountains, the Altun Mountains, and other highland mountains (Hou Y et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, meteorological observation stations are unevenly distributed, particularly in deserts and alpine regions. The TRB is dry and windy, with a large difference in daily temperature, scarce precipitation, abundant light and heat resources, and strong evaporation (Xu Z X et al. 2004).\u003c/p\u003e \u003cp\u003eIn order to quantify the differences in temperature and precipitation among the sub-basins of the TRB, this study utilizes Xinjiang\u0026rsquo;s water resource zoning boundaries and the 1:250,000 administrative map to delineate distinct regions within the TRB. Specifically, the division includes the Kaidu River Basin (R1), Weigan River Basin (R2), Aksu River Basin (R3), Kashgar River Basin (R4), Yarkand River Basin (R5), Hotan River Basin (R6), Keriya River Basin (R7), Cherchen River Basin (R8) and Tarim River Mainstream Region (R9) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, the study also delineates the Taklamakan Desert Region, the Kumtag Desert Region and the Western Qaidam Basin Desert Region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, due to limitations in natural conditions and observational data, these areas were not included in the analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWatershed zoning and distribution of meteorological stations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePrimary Zone\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e \u003cp\u003eTRB\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003esecondary Zone\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eR7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eR8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eR9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eNumber of sites\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTemperatures\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePrecipitation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"3. DATA AND METHODS","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Data sources\u003c/h2\u003e \u003cp\u003eIn this study, monthly temperature and precipitation data from 42 national meteorological stations in Xinjiang region from 1961 to 2021, as well as surface air temperature data, were used. Among them, the surface air temperature data were obtained from the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies (GISS) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.giss.nasa.gov/gistemp/\u003c/span\u003e\u003cspan address=\"https://data.giss.nasa.gov/gistemp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), while the monthly temperature and precipitation data were provided by the National Meteorological Information Center of China Meteorological Administration (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.nmic.cn/\u003c/span\u003e\u003cspan address=\"http://www.nmic.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To ensure long-term continuity and representativeness, each station required at least 52 years of observations with a minimum of 8 months of data per year. Observations with precipitation less than 0.1 mm were excluded to ensure data accuracy. After eliminating missing values and temporal inhomogeneities, data from 42 stations for temperature and 40 stations for precipitation from 1961 to 2021 were selected for analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Seasonal scales were assessed based on the traditional division of our weather service, which divides the four seasons into MAM (March to May), JJA (June to August), SON (September to November), and DJF (December to February).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Methods\u003c/h2\u003e \u003cp\u003eIn this study, we analyzed the temporal changes in temperature and precipitation in the TRB from 1961 to 2021 using linear trend analysis (Worku M A et al. 2022). We applied the Empirical Orthogonal Function (EOF) method to detect the temporal and spatial patterns of the TRB and performed significance testing on the spatial modes using North\u0026rsquo;s method (Xia Z et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, we used the Mann-Kendall (M-K) mutation test to identify abrupt changes and trends in temperature and precipitation (Alemu and Dioha \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, we calculated the correlation coefficients between the temperature and precipitation of the entire TRB and its sub-basins with the global mean temperature (GMT) to explore the response of the TRB\u0026rsquo;s temperature and precipitation to global warming (Aihaiti A et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.1 TRB temporal and spatial trends in temperature and precipitation\u003c/h2\u003e \u003cp\u003eIn the past 60 years, the average temperature of the TRB was 10.1 ℃, the highest average annual temperature was 11.3 ℃ in 2016, and the lowest was 8.9 ℃ in 1967. The difference between the highest and lowest average annual temperatures was 2.4 ℃, indicating significant variability in inter-annual temperatures. In the past 60 years, the average annual temperature of the TRB has shown a significant upward trend, with an increase rate of 0.2 ℃/10a (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). From 1961 to 2021, the TRB average annual precipitation was 74.2 mm. The maximum value of the average annual precipitation was 153.6 mm in 2010, and the minimum value of the average annual precipitation was 30.5 mm in 1985, with an extreme value ratio of 5.04 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The average annual precipitation has exhibited a general increasing trend, with a growth rate of 7.1 mm/10a.\u003c/p\u003e \u003cp\u003eThe spatial distribution of average annual temperature and annual precipitation was presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The average annual temperature of the TRB in the past 60 years was above 8 ℃. Among them, stations around the Tarim Basin Desert Area recorded average annual temperatures of 10 and 16\u0026deg;C. Meanwhile, stations in the northern and western fringes registered temperatures of 0 to 6\u0026deg;C during the same period (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). That is, the average annual temperature of the TRB was closely related to the topography, which was higher around the Tarim Basin Desert Area than in other regions. Over the last 60 years, the annual precipitation at most stations throughout the TRB was above 30 mm. The annual precipitation for the last 60 years at the stations in the northwestern fringe of the region were above 90 mm, while at the stations in the southeastern region was between 20 and 50 mm (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). That is, the annual precipitation showed a gradual decrease from northwest to southeast, with the northwest region higher than the southeast region in terms of spatial distribution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.2 TRB spatial and temporal modes of temperature and precipitation\u003c/h2\u003e \u003cp\u003eIn order to further study the spatial and temporal distribution characteristics of meteorological elements in the TRB, EOF analysis was conducted on the average annual temperature and annual precipitation data, followed by North\u0026rsquo;s significance test. This process identified the principal EOFs and their corresponding PCs in the TRB. The values and contributions of the first to fourth eigenvectors of average annual temperature and average annual precipitation for the TRB from 1961 to 2021 were shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The cumulative variance contribution of the first and second modes of average annual temperature amounted to 76.87% (significant at 0.05 level). For annual precipitation, it was 56.98% (significant at the 0.05 level). This indicated that the first and second eigenvalues could better reflect the type of spatial distribution of average annual temperature and annual precipitation in the TRB in the past 60 years, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariance contributions and accumulated variance contribution of the first to four eigenvectors of the EOF decomposition of average annual temperature and annual precipitation in the TRB from 1961 to 2021.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSerial\u003c/p\u003e \u003cp\u003enumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEOF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eVariance contribution\u003c/p\u003e \u003cp\u003erate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eCumulative variance contribution rate (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e69.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e46.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e76.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e56.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e82.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e62.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e86.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e66.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe eigenvector values of all stations in mode 1 of annual mean temperature were positive, indicating a highly consistent temperature change trend in the TRB from 1961 to 2021. That is, the temperature distribution characteristics of the entire basin were either entirely in high temperature or entirely in low temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The eigenvector value starts from the southern part of the TRB and gradually increases to the north. The high value center was located in the Kaidu River Basin, the Weigan River Basin and the northern part of the Yerqiang River Basin, indicating that this region had the highest average annual temperature and was the sensitive center of temperature change. In contrast, the low-value center was in the southern Yerqiang River Basin, southern Hetian River Basin, and Keriya River Basin. The variance contribution rate of mode 2 was 7.38%, which was also a typical spatial distribution of temperature in TRB. The Yerqiang River Basin, Hotan River Basin, western Chelchen River Basin, and Keriya River Basin showed reverse change characteristics compared to the Kaidu River Basin, Weigan River Basin, Aksu River Basin, eastern Chelchen River Basin, and Kashgar River Basin. The magnitude decreases gradually from south to north, indicating that the change of temperature in the basin decreases gradually from south to north. The high value center was in the southern part of TRB, and the low value center was in the northern part of TRB, showing a north-south reverse distribution pattern. That is, higher temperatures in the south corresponded to lower temperatures in the north, and vice versa.\u003c/p\u003e \u003cp\u003eThe time coefficient can reflect the time change characteristic corresponding to the spatial distribution mode of the eigenvector. A positive time coefficient indicates a change direction consistent with the spatial mode, while a negative value indicates the opposite. The greater the absolute value of the time coefficient, the greater the typical degree of the mode (Haynes and Beare \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The spatial distribution characteristics of average annual temperature in TRB were classified into four main types: high temperature in the whole basin, low temperature in the whole basin, high temperature in the south and low temperature in the north, and low temperature in the south and high temperature in the north (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e showed that the trend slope of the time coefficient for mode 1 was greater than zero, indicating to some extent that the annual average temperature of the basin has increased in the past 60 years. that was, mode 1 had a trend of high temperature of the whole basin. For mode 2, a positive time coefficient represents high temperature in the south and low temperature in the north, while a negative coefficient represents the reverse. The trend slope of time coefficient of mode 2 of annual rainfall in TRB was greater than zero, indicating that high temperature in the south and low temperature in the north were the main distribution types of the basin.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe eigenvector values of all stations in mode 1 of annual precipitation were positive, indicating a consistent variation trend in TRB\u0026rsquo;s precipitation from 1961 to 2021, with the entire basin being either rainy or less rainy (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). The eigenvector value starts from the western part of the basin and gradually increases to the east. The high value center was located in the western part of the Kaidu River Basin and the Cherchen River Basin, indicating that this region had the highest annual precipitation and was the sensitive center of precipitation change, while the low value center was located in the Yerqiang River Basin and the Kashgar River Basin. The variance contribution rate of mode 2 was 9.99%, which was also a typical spatial distribution form of precipitation in TRB. The Kashgar River Basin and Yerqiang River Basin showed opposite characteristics compared to the Kaidu River Basin, Weigan River Basin, and Aksu River Basin. The magnitude decreases gradually from west to east, indicating that the change of precipitation in the basin decreases gradually from west to east. The east-west distribution pattern was reversed, that was, higher precipitation in the west corresponded to lower precipitation in the east, and vice versa.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec shows that the trend slope of the time coefficient of mode 1 was greater than zero, indicating to a certain extent that the annual precipitation of the basin had an increasing trend in the past 60 years, that was, mode 1 had a tendency of rainfall in the whole basin. However, when the time coefficient of mode 2 was positive, the precipitation distribution was rainy in the west and rainy in the east; when the time coefficient was negative, it was rainy in the north and rainy in the west. The trend slope of the time coefficient of mode 2 of annual rainfall in TRB was greater than zero, which indicates that the main distribution type of the annual rainfall in the west was more rainy than that in the east.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Differences of temperature and precipitation in the sub-basins\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Differences of temporal trends\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrom 1961 to 2021, the average annual temperatures in R1 to R9 were 7.1 ℃, 10.6 ℃, 9.9 ℃, 9.6 ℃, 10.6 ℃, 12.2 ℃, 12.1 ℃, 11.3 ℃, and 11.0 ℃, respectively. There were differences in the occurrence time of the lowest and highest annual mean temperature in each sub-basins of TRB. Specifically, the lowest annual average temperature of R2 and R5 appeared in 1976 and 1969, at 9.4 ℃ and 8.1 ℃, respectively. The lowest annual mean temperature of R3 and R4 occurred in 1974, at 8.7 ℃ and 8.1 ℃ respectively. The lowest annual mean temperature of R1, R6, R7, R8 and R9 were all occurred in 1967, at 5.7 ℃, 10.9 ℃, 10.5 ℃, 9.6 ℃ and 9.8 ℃ respectively. On the other hand, the highest annual average temperature of R1, R2, R4 and R6 occurred in 2007, which were 8.4 ℃, 11.7 ℃, 10.8 ℃ and 13.6 ℃ respectively. The highest annual average temperature values of R3, R5, R7, R8 and R9 all occurred in 2016, which were 11.0 ℃, 12.2 ℃, 13.6 ℃, 12.6 ℃ and 12.4 ℃ respectively. Furthermore, the region with the largest difference between the highest and lowest annual mean temperatures was R5 (4.1 ℃), while the smallest difference was in R2 (2.3 ℃), indicating significant inter-annual temperature variations in each sub-basin.\u003c/p\u003e \u003cp\u003eThe linear trend of average annual temperature in each sub-basin showed a significant increase from 1961 to 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The average annual temperature increase rates in R1, R2, R3, R4, R8, and R9 were all 0.2 ℃/10a, while those in R5, R6, and R7 were 0.3 ℃/10a (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The difference between the highest and lowest rates of increase in average annual temperature was 0.1 ℃/10a. In addition, there were differences in the time of abrupt change of annual mean temperature in each branch basin. Compared with other regions, R5, R6, R7 and R9 had slightly later mean temperature change time, which was later than 2000 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe difference of climate tendency rate and abrupt change time of annual mean temperature and annual precipitation in each branch basin of TRB from 1961 to 2021.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003esecondary Zone\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eClimatic tendency rate\u003c/p\u003e \u003cp\u003e(℃/10a、mm/10a)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMutation time(year)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrecipitation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003etemperature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprecipitation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1988, 2009, 2010, 2011, 2012, 2018,2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1978, 2020, 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2003, 2006, 2008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2010, 2019, 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2010, 2011, 2012, 2013, 2016, 2019, 2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2010, 2011, 2012, 2020, 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1974, 1975, 1981, 1983, 1984, 1985, 1987, 2020, 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2008, 2009, 2010, 2011, 2012, 2020, 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom 1961 to 2021, the annual precipitation in R1 to R9 were 95.0 mm, 75.9 mm, 109.3 mm, 91.8 mm, 61.0 mm, 44.5 mm, 43.6 mm, 27.0 mm, and 42.7 mm, respectively. There were differences in the occurrence time of the lowest and highest annual precipitation in each sub-basins of TRB. Specifically, the lowest annual precipitation of R1, R3 and R4 appeared in 1985, respectively, which were 32.6 mm, 41.9mm and 24.9 mm. The lowest annual precipitation in R2 was 30.6 mm in 1965. The lowest annual precipitation in R2 was 30.6 mm in 1965. The lowest annual precipitation in R5 was 22.9 mm in 1978. The lowest annual precipitation in R6 was 6.9 mm in 1963. The lowest annual precipitation in R7 was 8 mm in 1986. The lowest annual precipitation of R8 and R9 occurred in 2001, 6.1 mm and 12.4 mm respectively. On the other hand, the highest annual precipitation in R1 was 173.5 mm in 2016. The highest annual precipitation in R2 was 159.8 mm in 1987. The highest annual precipitation of R3, R4, R5, R6 and R7 occurred in 2010, which were 225.4 mm, 230.4 mm, 146.6 mm, 138.6 mm and 153.1 mm respectively. The highest annual precipitation in R8 was 80.2 mm in 2005. The highest annual precipitation in R9 was 96.9 mm in 2015.\u003c/p\u003e \u003cp\u003eThe linear trend of annual precipitation in each sub-basin during 1961\u0026ndash;2021 showed an obvious increasing trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Compared with the annual mean temperature, the increase rate of annual precipitation in each sub-watershed was significantly different. The annual precipitation increase rates in regions R1 to R9 were 4.7 mm/10a, 6.0 mm/10a, 12.9 mm/10a, 8.8 mm/10a, 6.8 mm/10a, 7.4 mm/10a, 4.1 mm/10a, 1.9 mm/10a and 2.5 mm/10a. There was a difference of 11mm/10a between the region R3 with the largest annual precipitation growth rate and the region R8 with the smallest precipitation growth rate. In addition, compared with the annual average temperature, except for region 3, the annual precipitation of the other sub-basins had more abrupt points, which had obvious interannual variation (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Differences in spatial distribution\u003c/h2\u003e \u003cp\u003eDuring the past 60 years, the average annual temperature and annual precipitation in the TRB have shown significant spatial differentiation. When analyzed on an interannual scale, most of the average annual temperatures in the TRB were above 8\u0026deg;C. Among them, the average annual temperature in R1 to R4 lower than that in R5 to R9. On the other hand, the annual precipitation of R1 to R5 was higher than of R6 to R9.\u003c/p\u003e \u003cp\u003eFrom 1961 to 2021, the seasonal mean temperature of each branch basin showed the mean temperature in summer\u0026thinsp;\u0026gt;\u0026thinsp;mean temperature in spring\u0026thinsp;\u0026gt;\u0026thinsp;mean temperature in autumn\u0026thinsp;\u0026gt;\u0026thinsp;mean temperature in winter, but there were differences in each season. The average spring temperatures of R2, R6 and R7 were 13.91 ℃, 15.39 ℃ and 15.35 ℃, respectively, which were significantly higher than other regions in the same season. The mean summer temperatures of R6, R7, R8 and R9 were 24.35 ℃, 24.41 ℃, 25.41 ℃ and 25.07 ℃, respectively, which were significantly higher than other regions in the same season. The temperature of R6 and R7 in autumn was higher, 11.79 ℃ and 11.41 ℃ respectively, which was significantly higher than that of other regions in the same season. The average winter temperature of R6 and R7 was higher, -2.59 ℃ and \u0026minus;\u0026thinsp;2.83 ℃ respectively, which was significantly higher than that of other regions in the same season (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee). Among them, the average temperature of R1 in all seasons was significantly lower than that of other sub-basins, mainly because R1 contains an extreme low temperature point (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-d).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrom 1961 to 2021, the seasonal precipitation of each branch watershed showed the following pattern: summer precipitation\u0026thinsp;\u0026gt;\u0026thinsp;spring precipitation\u0026thinsp;\u0026gt;\u0026thinsp;autumn precipitation\u0026thinsp;\u0026gt;\u0026thinsp;winter precipitation, but there were differences in each season. Among them, the spring precipitation of R3 and R4 was 23.95 mm and 26.11 mm, respectively, which was significantly higher than other regions in the same season. The lowest spring precipitation was 4.51 mm in R8. The summer precipitation of R1 and R3 was 58.37 mm and 60.35 mm, respectively, which was significantly higher than that of other regions in the same season. R8 has the lowest summer precipitation at 18.93 mm. The autumn precipitation of R3 and R4 was 19.21 mm and 16.16 mm, respectively, which was significantly higher than other regions in the same season. R8 has the lowest precipitation in autumn at 1.57 mm. The winter precipitation of R4 was 10.07 mm, which was significantly higher than that of other regions in the same season (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ei). R9 had the lowest winter precipitation of 1.57 mm (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ej).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Possible impacts of global warming on the TRB\u003c/h2\u003e \u003cp\u003eThrough the above analysis, it was found that the temperature and precipitation in this basin both showed an increasing trend, and the trend was different in each branch basin. We thus analyzed the connection between this trend and global warming to identify if and where increasing global average temperatures were affecting temperature and precipitation in the TRB.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea showed the relationship between the annual average temperature of TRB and the global average temperature of land and ocean, with a correlation coefficient ranging from \u0026minus;\u0026thinsp;0.1 to 0.8. Notably, 97.6% of the sites exhibited a positive correlation, and 71.4% of the sites had a correlation coefficient greater than 0.5. This indicated that the annual mean temperature changes in most regions of the TRB were consistent with the global mean temperature changes, and there was little difference in response to global warming among the sub-basins.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb illustrated the relationship between annual precipitation of TRB and global average temperature of land and ocean. Compared to temperature, the response of precipitation to global warming was slightly lower, with correlation coefficients ranging from 0.1 to 0.6. All stations in the region showed a positive correlation between annual precipitation and global mean temperature. However, only 10% of the sites had a correlation coefficient greater than 0.5, while 82.5% had coefficients between 0.2 and 0.5. This suggested that while the annual precipitation trend aligns with global temperature changes, the relationship was weak. Compared with the average annual temperature, the correlation between annual precipitation and global warming was significantly different among the sub-basins, and the correlation between annual precipitation in R3 and global warming was higher than that in other sub-basins.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo gain a more comprehensive understanding of the impact of global warming on temperature and precipitation changes in the TRB, we further explored the relationship between seasonal mean temperatures and seasonal precipitation with the global mean temperature. This analysis provided a scientific basis for developing effective climate change adaptation strategies for the region.\u003c/p\u003e \u003cp\u003eThe seasonal mean temperature in spring and winter in the TRB was positively correlated with the global mean temperature, with correlation coefficients ranging from 0 to 0.7 and 0.1 to 0.6, respectively. The summer mean temperature at 86% of the sites and the autumn mean temperature at 88% of the sites were positively correlated with global mean temperature changes, indicating a significant impact of global warming on temperature variation in the TRB, especially in summer and autumn. In addition, the correlation between seasonal temperature variations and global mean temperature varied across different sub-basins (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ei). For example, the correlation coefficient between summer mean temperature and global mean temperature in each branch basin was quite different, and the difference between R8 with the highest correlation and R4 with the lowest correlation was 0.5. The correlation coefficient between autumn mean temperature and global mean temperature was strong, especially R5 (0.6) and R7 (0.7).\u003c/p\u003e \u003cp\u003eThe correlation coefficients between seasonal precipitation and global mean temperature in spring, summer, autumn and winter in TRB were 0 to 0.4, -0.1 to 0.5, -0.1 to 0.42 and \u0026minus;\u0026thinsp;0.1 to 0.3, respectively. The proportion of sites with positive correlations between seasonal precipitation and global mean temperature was 95% in spring, 90% in summer, 98% in autumn, and 87.5% in winter. Although the correlation between seasonal precipitation and global mean temperature in this basin was lower than that between seasonal mean temperature and global mean temperature, global warming still affects the seasonal precipitation in TRB, especially in summer and autumn. In addition, the correlation between seasonal precipitation variations and global mean temperature also varied among different sub-basins (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ej). For instance, the correlation coefficients between spring precipitation and global mean temperature ranged from 0.1 to 0.3. The correlation coefficients between summer precipitation and global mean temperature vary greatly among the sub-basins, among which R6 had the highest correlation with global mean temperature (0.43), and R8 had the lowest correlation with global mean temperature (0.04), with a difference of 0.39. There was a strong positive correlation between precipitation and global mean temperature change in most basins in autumn, especially R3 (0.4). The correlation between winter mean temperature and global mean temperature was the highest in R8 (0.2), while other sub-basins showed lower correlations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. DISCUSSION","content":"\u003cp\u003eStudies have shown that the rate of temperature increase in the TRB has been significant since the mid-1980s, with an increase of 0.2\u0026deg;C/10a. About 72.3% of the meteorological stations experienced a significant increase in precipitation, with an average rate of increase of 7.5 mm/10a (Li W et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This is the same as the overall trend of average of temperature and average of precipitation in the TRB obtained in this study. In particular, the growth rate of average annual temperature was slightly higher than the 0.2\u0026deg;C/10a derived in this study, and the growth rate of average annual precipitation was slightly higher than the 7.1 mm/10a derived in this study. This may be mainly because the two studies were on different time scales.\u003c/p\u003e \u003cp\u003eAt the same time, most of the previous studies focused on the whole region, and there were relatively few studies on the climatic elements of each branch basin. In addition, few scholars have applied the EOF method to the analysis of the spatio-temporal variation characteristics of temperature and precipitation in this basin. In this study, the temporal and spatial variations of average temperature and precipitation were analyzed using EOF. Based on the long time series observed data, the whole region was divided into secondary watersheds, such as R1 to R9. Furthermore, quantitatively analyzed the differences in the changes of their meteorological elements. Compared with the average annual temperature, the annual precipitation growth rate of each branch basin was different. There were also differences in the abrupt change time of average annual mild precipitation in each branch basin. This was at variance with the results of Zhang et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which showed that the abrupt change in precipitation over the whole region was concentrated in 1986. The main reasons for these differences may be that the time scales, regional division and the mutation tests methods of the studies are different. This study used the M-K mutation test, while Zhang et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) used the sliding mean difference test. In order to tap more accurate and effective climate change signals in the TRB, the applicability of different methods in the TRB and the reliability of the results need to be further explored.\u003c/p\u003e \u003cp\u003eIn this study, the climate station equipment in the middle TRB, where the Taklimakan Desert is located, is easily affected by extreme weather, resulting in the absence or incomplete climate station data in this region, which failed to analyze the temperature and precipitation changes in this region in detail. In the future, reanalysis data sets or remote sensing data sets can be used for further research to make up for the deficiency of ground climate station data.\u003c/p\u003e \u003cp\u003eIn summary, over the last 60 years, the TRB and its sub-basins have experienced a tendency towards warmth and humidity, but with regional differences. The rising temperatures and humidity in the basin provide more favorable conditions for extreme precipitation events (Li W et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To better cope with climate-related disasters, it is necessary to strengthen meteorological monitoring and numerical early warning for the basin and its sub-basins. Additionally, this study only analyzed the relationship between temperature, precipitation and global warming in this basin and its sub-basins. Future studies should further explore the regional impacts of climate change, changes in atmospheric circulation patterns, and the effects of human activities on temperature and precipitation in the TRB.\u003c/p\u003e"},{"header":"6. CONCLUSIONS","content":"\u003cp\u003eThe study utilized data from meteorological stations in the TRB from 1961 to 2021, and employed linear regression method, EOF and M-K mutation test for analysis. Firstly, the spatial and temporal distribution characteristics of average annual temperature and average annual precipitation in the TRB were analyzed. Secondly, the EOF was used to analyze the characteristics of TRB temporal and spatial modal changes. Thirdly, the differences in the spatial and temporal characteristics of meteorological elements in the whole basin and sub-basins of the TRB were quantitatively analyzed. Finally, the response of average temperature and precipitation to global warming in the whole basin and sub-basins of the TRB was quantitatively analyzed. The results showed that:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe average annual temperature and average annual precipitation of the TRB have shown an overall increasing trend over the past 60 years. The rate of increase was 0.2\u0026deg;C/10a and 7.1 mm/10a, respectively. Compared with the average annual temperature growth rate, the annual precipitation growth rate of each branch basin was quite different, and the difference between the region with the highest growth rate and the region with the lowest growth rate was 11.0 mm/10a. The high values of average annual temperature were mainly distributed in the western and southern stations of the TRB (10\u0026thinsp;~\u0026thinsp;16\u0026deg;C), and the high values of annual precipitation were mainly distributed in the western and northern stations (50\u0026thinsp;~\u0026thinsp;90 mm).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe first mode of the Empirical Orthogonal Function (EOF1) for both the average annual temperature and annual precipitation showed a consistent pattern, while EOF2 an opposite pattern. Combining the time coefficients of their modes yielded a significant increasing trend in average annual temperature and average annual precipitation for the TRB from 1961 to 2021.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe average annual temperature and annual precipitation in all sub-basins of the TRB have shown a significant increasing trend over the past 60 years, but there were regional differences. Among them, R5, R6 and R7 have higher warming rates, and the annual average precipitation increase rate and change range of each sub-basin were more significant than the temperature change, especially the annual average precipitation increase rate of R3 sub-basin was the largest. In addition, except R3, the average annual precipitation of other sub-basins had obvious inter-annual fluctuation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThere were obvious differences of temperature and precipitation in each branch basin during the season. In terms of temperature, the mean temperature in summer was generally higher, and R6, R7, R8 and R9 have the highest mean temperature in summer. In terms of spring mean temperature, R2, R6 and R7 were significantly higher than other regions. In terms of average temperature in autumn and winter, R6 and R7 also show higher values. In terms of precipitation, summer precipitation is generally large, especially R1 and R3; Spring precipitation is highest in R3 and R4. The precipitation in autumn is more evenly distributed, but R3 and R4 are still higher than other regions. In terms of winter precipitation, R4 has the highest precipitation, while R9 has the lowest. These results reflect the significant temporal and spatial variations of seasonal temperature and precipitation in different regions of TRB.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eA positive correlation was observed between the annual mean temperature of the TRB and global mean temperature changes, with most sites exhibiting a correlation coefficient greater than 0.5. In contrast, the correlation between annual precipitation and global mean temperature was weaker but still positive, with most sites having coefficients between 0.2 and 0.5. Seasonal mean temperatures in spring and winter showed significant positive correlations with global mean temperature, particularly in summer and autumn. Seasonal precipitation correlations were generally lower, but summer and autumn had more substantial impacts, especially in sub-basins like R6 and R3.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was funded by The Third Xinjiang Scientific Expedition, grant number 2022xjkk030502; Science and Technology Innovation Team (Tianshan Innovation Team) Project, grant number 2022TSYCTD0007; Innovation Team Project of Xinjiang Meteorological Service, grant number ZD202306.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and data curation were performed by Meiqi Song, Cong Wen, Yisilamu Wulayin, Jian Peng and Jiacheng Gao. The methodology was performed by Wen Huo, Fan Yang, Chenglong Zhou and Yu Wang. The investigation, visualization, writing - original draft preparation and analysis were performed by Siqi Wang, Ailiyaer Aihaiti, Yongqiang Liu, Hajigul Sayit and Mamtimin Ali. And all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability Statement:\u003c/h2\u003e \u003cp\u003eThe datasets used in this study can be provide by Mamtimin Ali ([email protected]) upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTollefson J (2022) Climate change is hitting the planet faster than scientists originally thought[J]. 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Water Power 46(04):28\u0026ndash;34 (In Chinese)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi W, Zhu C, Chen Y (2023) Recent changes in the water and ecological condition at the arid Tarim River Basin[J]\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tarim River Basin, precipitation, temperature, spatial and temporal heterogeneity","lastPublishedDoi":"10.21203/rs.3.rs-4717419/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4717419/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBased on the monthly temperature and precipitation observation data of 42 national meteorological stations in the Tarim River Basin (TRB) from 1961 to 2021, the spatiotemporal variation characteristics and differences of temperature and precipitation in the whole basin and its sub-basin were explored and quantified. The results showed that: 1) The average annual temperature and annual precipitation increase rate were 0.2 ℃/10a and 7.1 mm/10a during 1961 to 2021, respectively, with significant spatial and temporal distribution differences. 2) The first mode of the Empirical Orthogonal Function (EOF1) for both temperature and precipitation showed a consistent pattern, while EOF2 showed an opposite pattern. 3) In the TRB sub-basin, the difference between the highest and lowest annual average temperature increase rates was 0.1 ℃/10a. Similarly, the difference between the highest annual precipitation increase rates (in the Aksu River Basin) and lowest (in the Cherchen River Basin and Tarim River Mainstream Region) was 0.9 mm/10a. 4) The Kaidu River Basin had a significantly lower winter mean temperature of -9.69 ℃ compared to other sub-basins. Additionally, seasonal precipitation varied greatly among sub-basins, particularly in summer. 5) The annual mean temperature showed a strong positive correlation with the global mean temperature (coefficients over 0.5 for most sites), while the correlation for annual precipitation was weaker but still positive, ranging from 0.2 to 0.5. Significant positive correlations were observed for seasonal mean temperatures, especially in summer and autumn. Seasonal precipitation correlations were generally lower but had notable impacts in summer and autumn, particularly in sub-basins like Hotan River Basin and Aksu River Basin.\u003c/p\u003e","manuscriptTitle":"The increase of temperature and precipitation in the different regions of Tarim River Basin has spatial and temporal heterogeneity over 1961-2021.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-05 13:05:19","doi":"10.21203/rs.3.rs-4717419/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"326f7d50-2059-4363-9f00-430f711f141f","owner":[],"postedDate":"August 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-29T11:22:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-05 13:05:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4717419","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4717419","identity":"rs-4717419","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}