China has a vast cotton region. Cotton is cultivated within 20°–46°N and 76°–124°E. There are three dominant cotton-producing regions in the country, namely, the Yangtze Valley cotton region (a subtropical monsoon climate), the Huanghe Valley cotton region (a temperate monsoon climate predominates), and the northwestern inland cotton region (a temperate continental climate). The Northwestern inland cotton region includes three subregions: the Eastern Xinjiang subregion, the Southern Xinjiang subregion, and the Northern Xinjiang subregion. The spatial distribution of China’s main cotton regions and meteorological stations is shown in Figure 1.

The data were mainly the routinely collected meteorological data from 248 standard meteorological stations in the main cotton regions of China from 1960 to 2019, including the daily minimum air temperature (T_min, °C), daily maximum air temperature (T_max, °C), sunshine duration hours (SD, h), wind speed at 2 m height (WS, m/s), and relative humidity (RH, %). The data were mainly obtained from the China Meteorological Science Data Sharing Service Network (http://data.cma.cn/); the time-series daily meteorological data that were used were long and continuous.

The research framework is shown in Figure 2. ET_O was calculated using the CropWat 8.0 model. The model calculates ETo using the Penman–Monteith equation. It was recommended as the sole standard method for ET_O estimation by FAO in 1998 (Allen et al., 1998), which formula has been widely used (Fan and Thomas, 2013; Singh et al., 2022).

The equation is as follows: E T O = 0.408 ∆ R n − G + 900 T + 273 u 2 e s − e a ∆ + r 1 + 0.34 u 2 (1) where ET_O is the daily reference evapotranspiration [mm/d], R_n is the net radiation at the crop surface [MJ/(m² · d)], G is the soil heat flux density [MJ/(m² · d)], T is the mean daily air temperature at 2 m height [°C], U₂ is the wind speed at 2 m height [m/s], e_s is the saturation vapor pressure [kPa], e_a is the actual vapor pressure [kPa], e_s-e_a is the saturation vapor pressure deficit [kPa], Δ is the slope vapor pressure curve [kPa/°C], and γ is the psychrometric constant [kPa/°C].

ET_C was calculated, and the daily ET_O time series was multiplied using crop coefficient (Kc) (Table 1) values (Yang et al., 2021).

The equation is as follows: E T C = E T O ∙ K C (2) where Kc (Table 1) is the crop coefficient that converts ET_O into ETc requirements.

The Mann-Kendall trend test was used to analyze the evolution process and characteristics of ETo and ET_C and their related meteorological elements in the study area (Zhang et al., 2010). In the Mann-Kendall test, it was originally assumed that H₀: the time series data (x₁,…, x_n) were n independent samples with the same distribution of random variables. An alternative hypothesis H1 was a two-sided test: for all k, j≤n and k ≠ j, the distributions of x and xj are not the same, a mutation test: let the climatic sequences x₁, x₂,…, x_n, where S_k represents the ith sample, and X_i > X_j (1 ≤ j ≤ i) is the cumulative number. Sk was defined as follows: S K = ∑ i = 1 k 1 , x i > x j 0 , x i > x j , j = 1,2 , ⋯ , i ; k = 1,2 , ⋯ , n (3)

Under the assumption of stochastic independence of the time series, the mean and variance of S_k was as follows: E S k = k k − 1 4 , v a r S k = k k − 1 2 k + 5 72 , 1 ≤ k ≤ n (4)

Sk was standardized as follows: U F k = S k − E S k v a r S k (5)

UF_K is a normal distribution of standard given significance and α is a given significance level; if |UF_K| >U_α/2 (U_α/2 values can be found in the standard normal distribution chart. When taking a = 5% as the significance level, the corresponding value of U_α/2 is 1.96), it indicates that there is a significant trend change in the series. The time series x is arranged in reverse order and then calculated according to the above equation and at the same time: U B k = − U F k k = n + 1 − k   k = 1,2 , ⋯ , n , (6)

The sensitivity of ET_O to changes in climatic variables was investigated using the dimensionless relative sensitivity coefficient method (hereafter referred to as the sensitivity coefficient) based on the Penman–Monteith formula (McCuen, 1974). If S_vi > 0, it means that the ET_O has a positive sensitivity to the variation in the meteorological factor; if S_vi < 0, it means that the ET_O has a negative sensitivity. If | S_vi | is larger, it indicates that changes in climate change have a greater impact on ET_O (Supplementray Table S1).

The equation is shown below: S v i = lim ∆ v i → 0 ∆ E T 0 E T 0 ∆ v i v i = ∂ E T 0 ∂ v i ∙ v i E T 0 (7) where S_vi is the sensitivity coefficient of meteorological factor vi, ΔET_O is the variation in reference evapotranspiration, vi is the meteorological factor, and Δvi is the variation in the meteorological factor.

The contribution of climatic variables to ET_O variation was derived from the product of the multiyear relative rate of change and sensitivity. If C_vi>0, this means that the factor had a positive contribution to the variation in ET_O. If C_vi<0, the changes in the factor decreased ET_O, and the factor had a negative contribution. C v i = S v i ∙ R c v i (8) R c v i = n ∙ T r e n d v i a v i (9) where C_vi is the contribution rate of meteorological factor vi to ET_O variation, %; RC_vi is the relative change rate of vi, %; n is the number of years, which is 60 in this paper; avi is the mean value of vi; and Trend_vi is the annual trend of vi, calculated by the trend analysis method.

The CropWat 8.0 model, Python, and Microsoft Excel 2016 were used for data processing and correlation analysis. The spatial distributions of Evapotranspiration (ET_O) and Evapotranspiration Coefficient (ET_C) were expressed using the inverse distance weighting method (IDW) interpolation techniques of ArcGIS 10.2, in conjunction with Origin 2018. Simultaneously, the variations of climatic factors were depicted using Kriging interpolation methods.

The spatial distribution of climatic variables in the growth period of cotton in the past 60 years was different in China (Supplementary Figure S1). The highest values of T_min, T_max, and RH all appeared in the Yangtze Valley cotton region, with values of 27.42°C, 18.99°C, and 78.32%, respectively. T_min and RH had the lowest values in the northwestern inland cotton region, with values of 11.23°C and 42.92%, respectively, and T_max had the lowest value in the Huanghe Valley cotton region (24.75°C) (Figures 2A,B,E). The spatial distributions of SD and WS were the opposite of those of T_min and RH; maximum values were mainly distributed in the northwestern inland cotton region, and the values in the eastern Xinjiang subregion were the highest at 9.16 h and 2.75 m/s, respectively, while the values in the Yangtze Valley cotton region were the lowest at 5.30 h and 1.97 m/s, respectively. In addition, there were differences in the spatial distribution of climatic variables in different growth stages of cotton (Supplementary Figures S1-S6). The T_min, T_max, and SD in the mid-season stage of cotton were significantly higher than those in the other growth stages.

The T_min and T_max generally showed an upward trend, while WS and RH showed a downward trend in the cotton regions of China in the past 60 years. SD showed a downward trend in the Huanghe Valley and Yangtze Valley cotton regions and had an upward trend in the northwestern inland cotton region (Figure 3). In addition to T_min in the Yangtze Valley cotton region, the mutation times of T_min and T_max in the cotton regions of China were mainly concentrated in approximately 1996 and showed a significant upward trend after mutation. The increasing rates of T_min were 0.31°C/decade, 0.22°C/decade, 0.52°C/decade, 0.38°C/decade, and 0.42°C/decade in the Huanghe Valley, Yangtze Valley, and northwestern inland cotton regions (East Xinjiang subregion, South Xinjiang subregion, and North Xinjiang subregion), respectively, and the increasing rates of T_max were 0.19°C/decade, 0.21°C/decade, 0.32°C/decade, 0.19°C/decade, and 0.18°C/decade, respectively (Supplementray Table S2). The SD showed a decreasing trend at rates of −0.16 h/decade and −0.14 h/decade in the Huanghe Valley and Yangtze Valley cotton regions, respectively, and showed an increasing trend of 0.01 h/decade in the northwestern inland cotton region. WS had the largest rate of decline in the Huanghe Valley cotton region, followed by the northwestern inland cotton region and the Yangtze Valley cotton region, which had values of 0.19 m/s/decade >0.11 m/s/decade >0.07 m/s/decade. RH had a downward trend after 2010 in China’s major cotton planting regions, and the average change rates were −0.58%/decade, −0.51%/decade, and −0.32%/decade in the Huanghe Valley, the Yangtze Valley, and the northwestern inland cotton regions, respectively.

The distribution of ET_O and ET_C in the growing season showed an obvious spatial gradient (Figure 4; Figure 5). The ET_O and ET_C values decreased from west to east and from north to south. The ET_O and ET_C values in the northwestern inland cotton region were higher than those in the Huanghe Valley and Yangtze Valley cotton regions. The ET_O values were 978 mm, 778 mm, and 739 mm in the northwestern inland cotton region, Huanghe Valley cotton region, and Yangtze Valley cotton region, respectively, and the ET_C values were 891 mm, 663 mm, and 628 mm, respectively (Figure 4A; Figure 5A). In particular, the eastern Xinjiang subregion of the northwestern inland cotton region had the highest values of ET_O and ET_C, with average values of 1,060 mm and 981 mm, respectively, while the ET_O and ET_C were lowest in the western Yangtze Valley cotton region, with average values of 609 mm and 506 mm, respectively. The whole growth period of cotton was divided into four stages. The ET_O and ET_C of cotton first increased and then decreased during the growth period, and the maximum values occurred in the mid-season stage (Figures 4B–E; Figures 5B–E). The value of ET_O accounted for 33.91%, 32.20%, and 38.36% of the total growth period, respectively, and the value of ET_C accounted for 48.45%, 43.26%, and 51.27% of the total growth period, respectively.

ET_O and ET_C showed a decreasing trend in the major cotton regions in China from 1960 to 2019 (Figure 6). However, the decreasing trend was different in each cotton region. The ET_O and ET_C of the Huanghe Valley cotton region decreased from 787.23 mm and 677.62 mm in 1960–1979 to 769.84 mm and 654.33 mm in 2000–2019, respectively, at a rate of −3.49 mm/decade and −4.83 mm/decade. The ET_O and ET_C of the Yangtze Valley cotton region decreased from 749.19 mm and 653.02 mm in 1960–1979 to 735.01 mm and 625.50 mm in 2000–2019, respectively, at a rate of −3.98 mm/decade and −6.97 mm/decade, respectively (Supplementray Table S3). The mutation time of ET_O and ET_C occurred in approximately 1973 in the Huanghe Valley and Yangtze Valley cotton regions and showed a downward trend after the mutation occurred. The northwestern inland cotton region showed a downward trend overall, and the rates of decline of ET_O and ET_C were −0.14 mm/decade and −0.61 mm/decade, respectively. However, the northwestern inland cotton region showed an upward trend after 2000. Changes between the subregions were different, and the change rates of ET_O in the eastern, southern, and northern subregions were 2.49 mm/decade, −2.07 mm/decade, and −6.45 mm/decade, respectively; the change rates of ET_C were −1.96 mm/decade, −2.14 mm/decade, and −14.05 mm/decade, respectively. However, the ET_O and ET_C in cotton regions in China showed an upward trend from 2000 to 2019 except for the northern Xinjiang subregion of the northwestern inland cotton region, with an upward trend in the eastern Xinjiang subregion.

Climate change will affect the regional ET_O. The sensitivity of ET_O to climatic variables was explored by means of sensitivity coefficients to further understand the influence of climatic variables on ET_O. ET_O was most sensitive to T_max in the cotton region of China from 1960 to 2019, and its average sensitivity coefficient was 0.49, followed by RH (−0.45), SD (0.27), WS (0.19), and T_min (0.14) (Figure 7). In addition, the sensitivity coefficients for RH had a negative correlation, the sensitivity coefficients for T_min, T_max, and SD had a positive correlation, the sensitivity coefficients for WS were positive in 99% and negative in 1%, and the negative correlation region was mainly concentrated in the south of the Yangtze Valley cotton region. The sensitivity of ET_O to climatic variables in cotton regions had certain differences due to the geographical distribution. The most sensitive climatic variable was RH, followed by T_max, SD, T_min, and WS in the Huanghe Valley cotton region and Yangtze Valley cotton region from 1960 to 2019. The most sensitive climatic variable was T_max, followed by RH, WS, SD, and T_min in the northwestern inland cotton region from 1960 to 2019. ET_O was most sensitive to T_max change in the Yangtze Valley cotton region (0.54), followed by the northwestern inland cotton region (0.50) and the Huanghe Valley cotton region (0.45). The sensitivity of ET_O to T_min, RH, and SD decreased gradually from the Yangtze Valley cotton region to the northwest, showing a step-like decline. The highest sensitivity coefficient values in the cotton region of the Yangtze River basin were 0.27, −0.87, and 0.37. The lowest values were in the northwestern inland cotton region, which were 0.09, −0.28, and 0.24. The spatial distribution of the sensitivity of ET_O to WS was the opposite of T_min, RH, and SD, showing a step-like decline from the northwestern inland cotton region to the southeast. The sensitivity coefficient was the highest in the northwestern inland cotton region (0.25) and the lowest in the Yangtze Valley cotton region (0.09).

The spatial distribution of ET_O sensitivity to climate changes in different growth stages was similar to that in the whole growth period in each cotton region (Figure 8). The sensitivity of ET_O to climatic variables first increased and then decreased with the growth of cotton. The sensitivity of T_min, T_max, RH, and SD was the highest in the mid-season stage, with values of 0.18, 0.56, −0.47, and 0.33, respectively, and the growth stage with the lowest sensitivity coefficient was the initial period, with sensitivity coefficients of 0.10, 0.48, −0.44, and 0.23, respectively (Figures 8A–C). The highest sensitivity coefficient for ET_O to WS was in the development stage, with a sensitivity coefficient of 0.22, and the lowest sensitivity coefficient was in the late season stage, with a sensitivity coefficient of 0.15 (Figure 8D).

To identify the main climatic variables affecting ET_O changes in cotton in China and its three cotton regions, the relative contribution method was adopted to quantify the contribution of climatic variables to ET_O change (Figure 9). WS was an important climatic variable affecting the growing season ET_O trends, causing a reduction in ET_O by −4.26%, thereby becoming the largest contributor to the decreasing growing season ET_O from 1960 to 2019 in the cotton region of China. In addition, SD had a negative impact on ET_O, with a contribution rate of −2.22%. T_max was the crucial contributor to the increase in ET_O, with a contribution rate of 2.59%, followed by RH and T_min, with contribution rates of 2.02% and 1.99%, respectively, in the cotton region of China. However, the same climate variable might have different contribution rates to the change in ET_O in the three cotton regions because of different sensitivities and changes in climate change in terms of spatial distribution. T_max, T_min, and RH had positive contributions to ET_O during the growing season, and WS had negative contributions to ET_O. However, SD had a negative contribution to ET_O in the Huanghe Valley and the Yangtze Valley cotton regions but had a positive contribution to ET_O in the northwestern inland cotton region. SD was the meteorological factor with the largest contribution rate to ET_O changes in the Huanghe Valley and the Yangtze Valley cotton regions, with contribution rates of −7.02 and −4.40, respectively. WS was the meteorological factor with the largest contribution rate to ET_O changes in the northwestern inland cotton region, and the contribution rates were −4.16, −3.93, and −8.59 in the eastern Xinjiang subregion, southern Xinjiang subregion, and northern Xinjiang subregion, respectively.

The contribution rates of T_min, T_max, and RH to ET_O trends were highest in the initial period, with values of 2.22%, 3.63%, and 3.94%, respectively. The contribution rates of T_min and T_max were the lowest in the development stage, with values of 1.83% and 2.05%, respectively. The contribution rate of RH was the lowest in the mid-season stage (1.41%). The contribution rate of WS was the highest in the development stage (−5.05%) and the lowest in the late season stage (−3.34%). The contribution rate of SD was the highest in the mid-season stage (−4.47) and the lowest values were in the initial period (0.91%) (Figures 9B–E).

Climate change in China’s cotton regions was a microcosm of overall climate change in China. With the increase in population and the acceleration of industrialization, the emission of pollutants gradually increased, and aggregated aerosols from anthropogenic emissions of pollutants were the main dimming factor (Feng et al., 2017). The increase in aerosols weakens the direct solar radiation, resulting in a reduced SD. The Huanghe Valley and Yangtze Valley cotton regions are located in low-altitude areas, with high population density, concentrated human activities, and developed industry, and pollution is more serious than that in the northwestern inland cotton region, which has a low population density. Therefore, the declining trend of SD in the Huanghe Valley and Yangtze Valley cotton regions is more obvious than that in the northwestern inland cotton region. The changing trend of WS was consistent with the research of Jiang et al. from 1960 to 2000 (Li et al., 2014; Jiang et al., 2019); however, the research results were different after 2000. Contrary to the results of the gradual increase in WS in recent years in the research of scholars such as Sun et al. (2013), the WS of the main cotton areas in China showed a significant downward trend. There were two main reasons for the decline in WS. First, the weakening of the atmospheric circulation and the significant increase in temperature in the cotton regions of China (McVicar et al., 2012) changed the pressure difference that formed the atmospheric circulation and slowed the wind speed. Additionally, an increase in human activities and vegetation coverage and an increase in surface roughness resulted from human activities, such as planting and urbanization (Liu et al., 2013; Shi et al., 2017).

Temperature showed a significant upward trend in China’s major cotton regions. Aerosols and increasing greenhouse gases are the main causes of the global temperature rise (Najafi et al., 2015). The upward trend of T_min is more obvious than that of T_max. Aerosols and greenhouse gases absorb most of the solar radiation during the day and release energy into the atmosphere in the form of longwave radiation at night (Jiang et al., 2019). The changing trend of RH between 1960 and 2000 was not obvious, but in recent years, the downward trend has gradually increased. This result was consistent with previous research results (Li et al., 2017; Yang et al., 2021), which were related to the significant increase in temperature in recent years.

ET_O and ETc play an important role in agricultural water resource dispatching, irrigation system formulation, and farmland water management (Reddy, 2015; Pandey et al., 2016). Only comprehensively exploring the temporal and spatial evolution of ET_O that is influenced by climatic conditions and ETc that is influenced by the physical characteristics of the crop itself can improve well-irrigated and agricultural water management. The study of ET_O and ETc of major cotton regions in China showed the highest in the Northwest inland cotton regions and the lowest in the Yangtze Valley cotton regions. The reliability of this study’s conclusion is reinforced as Li et al.’s (2017) results aligned with ours. Liu et al. (2022) found that the range of ET_O of the spring wheat planting area was 460.6–809.3 mm in the Huanghe Valley Basin, which is also consistent with the present study. However, Yang et al. (2022) estimated that the ETc of cotton ranged from 551 to 606 mm in the North China Plain, which is lower than the results of this study. Mainly, the North China Plain is a sub-region of the Huanghe Valley cotton regions, and ETc in places such as Shaanxi located in the western part of the Huanghe Valley cotton region is higher than in the region where the North China Plain is located. Yang et al. (2022) indicated that the maximum value of ETc shifted from the south to the west of the North China Plain, which also proved this point.

Studies have shown that the ET_O and ET_C in China’s major cotton regions showed a downward trend, which was consistent with the “evaporation paradox” of many studies (Roderick and Farquhar, 2002; Li et al., 2012; Jiang et al., 2019). However, ET_O and ET_C in the Inland Northwest cotton regions showed an upward trend after 2000. Li et al. (2014) pointed out that the annual ET_O showed an upward trend from 2000 to 2009. In Southwest China, Li et al. (2014) pointed out that from 1958 to 1993, the ET_O level decreased and then increased in Northwest China, which was consistent with the results obtained in this study. The ET_O and ET_C showed differences in different growth stages of cotton. With the increase in the cotton growth period, the ET_O and ET_C of cotton first increased and then decreased, and the value was the largest in the mid-season. Cotton was in the reproductive growth stage at this stage, with relatively active physiological and biochemical activities, increased evapotranspiration, and constantly increased water demand, which promoted the ET_O and ET_C at this stage to be significantly higher than those at other stages in the cotton growing period. The most critical water requirement of cotton was in June at its blossoming and boll-forming stage (Yang et al., 2021). Therefore, irrigation should be increased during the rapid development stage or mid-season stage to ensure normal water demand.

Trends and fluctuations in climatic factors lead to variations in ET_O and ETc (Yang et al., 2021). Exploring the effects of climate change on ET_O changes can help predict ET_O changes in the context of climate change (Feng et al., 2017). The study showed that the sensitivity of the climatic variables in the cotton areas of China was ranked as follows: T_max (0.49) > RH (|−0.45|) > SD (0.27) > WS (0.19) > T_min (0.14). However, the vast span of the cotton region in China necessitates regional studies due to the diversity of regional geographical and climatic conditions. The most sensitive climatic variable was RH in the Huanghe Valley cotton region and Yangtze Valley cotton region, and the most sensitive climatic variable was T_max in the northwestern inland cotton region. This result was similar to previous studies. Zuo et al. (2012) and Jiang et al. (2019) found that RH was the most sensitive meteorological factor to ET_O changes in the Huanghe Valley and southern China. Li et al. (2014) found that T_max was the most sensitive meteorological factor to ET_O changes in the northwestern inland. In addition, the coefficients of T_min, T_max, and SD for the mid-season stage were maximized, which is similar to the study by Wang et al. (2014).

The contribution rate of climate change depends not only on the sensitivity of ET_O to climate change but also on the magnitude of the trend of climate change (Li et al., 2017). The sensitivity coefficient of T_max was the highest, but the variation range was limited, so the contribution rate of T_max to the variation in ET_O was not the highest. The largest contribution to ET_O was WS (−4.26%), followed by T_max (2.58%) > SD (−2.23%) > RH (2.07%) > T_min (2.00%). However, due to the difference in the geographical environment, the contribution to ET_O in different cotton areas was different. Wang et al. (2017) pointed out that SD had the greatest impact on ET_O in the eastern part of the Huanghe River and southern China, and WS played an important role in the change in ET_O in northwestern China, which is consistent with this study. The decrease in SD resulted in a decrease in energy reaching the leaves. The reduction in WS reduces the diffusion of water molecules through turbulent flow, resulting in a further reduction in evaporative demand, and the decline rate of WS and SD is higher than the increase rate of temperature in most cotton regions, so the decline in WS and SD offsets the effect of temperature on ET_O and ET_C changes (Sun et al., 2013; Wang et al., 2017; Yang et al., 2022). However, the declining trend of ET_O and ET_C has been gradually broken by the decrease in RH in recent years. The decrease in RH will reduce atmospheric vapor pressure and accelerate the release of water vapor from cotton stomata (Jiang et al., 2019). In addition, under the background of a continuous temperature increase, the ET_O and ET_C of the cotton regions may increase in the future, especially the northwestern inland cotton region. The characteristics of the stages of growth of cotton ET_O and ET_C evolution and influencing factors of the study are indispensable in the context of climate change, and the analysis methods of the sensitivity coefficient and contribution rate can be extended to other crops.

As widely recognized, climate change significantly impacts crop growth by influencing the hydrological cycle (Zeng et al., 2021). Understanding the changes in ET_O and ET_C is crucial for scientifically managing water resources and promoting sustainable agricultural production. However, some studies have suggested that the effect of climate change on agriculture may be limited (Piao et al., 2010). In this study, we focused solely on the influence of climate factors on ET_O and ET_C, overlooking the essential roles that different crop varieties and management practices play in shaping ET_O and ET_C of cotton. Moving forward, we intend to explore the intricate interplay between genes, management measures, environmental factors, and other variables affecting ET_O and ET_C of cotton. Moreover, cotton, being a water-intensive crop, is susceptible to water stress, making it imperative to investigate strategies to reduce ET_O and ET_C in cotton production. It is important to note that this study solely presents a sensitivity analysis of local meteorological factors on ET_O changes. We did not take into account the far-reaching effects of large-scale climate variability, which originates from the oceans and serves as critical drivers of global and regional climate change. These effects can have significant impacts on the evolutionary pattern of ET_O (Fan et al., 2016; Liu et al., 2018). Through ongoing research, we aim to gain a more comprehensive understanding of the multifaceted factors influencing ET_O and ET_C of cotton, thereby contributing to informed agricultural water management and adaptive strategies in the face of evolving climate conditions.