**4. Discussion**

This study analyzed the long-term spatiotemporal dynamics of terrestrial climate variables from an interannual and seasonal perspective over the TRHR of China. The rising Ta and P and decline in the RH and Rs over the TRHR were similar to the trends observed over the Tibetan Plateau (TP), where annual Ta and P increased by 0.447 ◦C and 9.97 mm per decade at the 27 meteorological stations during 1961–2001, as reported by Xu et al. [50]. Using observations from 78 China Meteorological Administration (CMA) stations, Yang et al. [63] also demonstrated that the TP experienced a rapid warming and wetting tendency in the period of 1984–2006. The overall rapid climate warming tendency over the TP has been demonstrated in numerous studies by observing stations [51,64,65], by oxygen isotope analysis of ice cores [66], and by satellite remote sensing products [67,68], with the observed warming rate ranging from 0.16 to 0.67 ◦C per decade during the past few decades [69]. In comparison with previous studies regarding climate change, our findings improve the spatial information over heterogeneous landscapes and present long-term distribution patterns of annual and seasonal climate variables at a regional scale and provide a new understanding of the climate change in the TRHR in recent years.

Climate change has an inevitable and significant impact on vegetation dynamics, particularly in the extremely sensitive ecosystem of the TRHR. The response of vegetation to climate change has been discussed by many studies, where the results differed according to the different vegetation types, the plant physiological processes, and environmental factors. Previous studies have pointed out that CO2 fertilization effects explain 70% of the observed greening trend in the tropics, whereas climate change contributes most to the vegetation greening of the TP [70]. Du et al. [71] proposed that solar radiation was the key factor governing the vegetation greening on the TP. This result is reasonable because sufficient solar radiation can promote the photosynthesis and respiration of vegetation, which is beneficial to plant growth [72]. According to Xu et al. [30], the averaged NDVI of the growing season was positively correlated with the summer Ta in the high-cold region, which indicated that the response of vegetation to temperature was likely to be more intense under climate warming. This conclusion supported our results to a certain extent. We found that when energy was the limiting factor for vegetation growth, Ta was a considerably more important driving factor than water. However, the effect of temperature on vegetation was obviously less than that of moisture in the water limiting area. These results can be explained by the fact that the climate condition in the TRHR is characterized by relatively abundant P during the growing season and lower temperature across the whole year [73]. We can conclude that increases in either Ta or P are predicted to have a positive influence on vegetation greening. These findings are in line with emerging evidence that the potential benefits from the climate "warming and moisture" trend are closely related to the increment of vegetation through alteration of vegetation phenology and prolonged growing season length in the TRHR [74].

The upward trend of ET we reported is consistent with long-term trend analysis, which indicated that the ET has significantly increased since the 1960s, especially in the central area of the TP [61,69,75]. The rising trend of ET corresponded to the significant increase in precipitation, the reduction of RH, and sunshine duration during the same period over the TP [76]. Yin et al. [77] suggested that the upward trend of ET was mainly constrained by the soil water supply, and linked with increased P, which is consistent with our results. In the arid and semi-arid regions, the increased P promoted the water availability for ET, resulting in the increment of ET. The pattern of increasing ET was matched by an increasing P in the western part of the TRHR, which was also confirmed by Yao et al. [29], who reported that P was the primary contributor to increasing ET during 1982–2010. However, in well-watered regions, climate (Ta, RH) and vegetation factors were considered to be related more to the ET dynamic. This result also agrees well with the study by Song et al. [78] on the TP, where dependencies of ET on leaf area index (LAI) and Ta appeared to be largely independent of moisture supply. Atmospheric demand was recognized as an important controlling factor on the long-term variations of ET. This inconsistent result can be explained by the di fferent atmospheric energy demand or surface moisture supply in di fferent regions [79]. In addition, the land use and land cover change (LUCC), and anthropogenic activities, such as agriculture irrigation and a fforestation projects, also have a substantial influence on the variation of ET [80].

A long-term spatiotemporal biophysical dynamic provides more accurate estimates of climate change, vegetation greening, and ET variation in the TRHR. Although several products have been extensively validated and confirmed in di fferent scales and regions, considerable uncertainties still exist. Regarding the climate forcing dataset, the accuracy of the reanalysis may be limited by the scarce measurements in the TRHR. Yang [81] et al. compared the shortwave radiation data of the CMFD product against the 579 in situ observations in China and found that the CMFD provided the closest match with ground measurements, with a 0.02 relative bias and a 5.6 root-mean-square error (RMSE) during 2008–2010. However, the precipitation data were detected to have an abnormal underestimation after August 2014. The inaccuracy of the precipitation data was also evaluated by Wang et al. [82], who found that the precipitation was overestimated at 90 stations over the TP. The biases of the CMFD dataset led to substantial errors in climate variation. Aside from the climate dataset, the uncertainties were also associated with the GIMMS NDVI data series driven by AVHRR. Kern et al. [83] suggested that there was a significant disagreement relationship between AVHRR NDVI3g and the MODIS NDVI dataset. Moreover, the influence of the canopy and soil background, aerosol e ffects, and cloud contamination were not completely eliminated due to the limitations of the AVHRR instruments [43,84]. The modified satellite-based Priestley–Taylor (MS-PT) algorithm produced a more accurate product as daily ET estimates exhibited a higher R<sup>2</sup> (0.87) and lower RMSE (12.5 <sup>W</sup>/m2) than the original PT algorithm in regional ET simulations. However, there are still large uncertainties due to the di fferent parameterization schemes of evaporation constraint. One limitation of the MS-PT product is that it shows large di fferences in daily ET estimates among the di fferent ecosystem types [85]. Yao et al. [86] evaluated the performance of PT products at di fferent biomes and demonstrated that the MS-PT model performed better in forest and village sites, with a higher R<sup>2</sup> of 0.93 and lower RMSE of 11.9 <sup>W</sup>/m2, whereas in grassland sites, the algorithms may not capture the soil moisture constraint, resulting in underestimating the ET value, which makes the simulated ET value uncertain in the alpine grassland ecosystems of the TRHR.
