The impacts of climate change on ecosystem productivity and water resources over a long term in China are not well quantified. Precipitation-use efficiency (PUE) is a key parameter that describes carbon and water exchange in terrestrial ecosystems. Research on the response of regional PUE to climate change and its driving forces is of great significance to climate-change mitigation and the sustainable development of regional ecology. Based on an improved actual evapotranspiration (ET_a) model, the responses of ET_a, net primary productivity (NPP), and PUE to climate change in different climatic regions of China were analyzed; the contributions of various environmental factors to PUE changes were quantified; and the conversion characteristics and regulatory mechanisms of the PUE regime in different climatic regions were identified. The results indicate that the improved ET_a model, after considering the limiting effect of energy on ET_a in humid regions, can simulate the ET_a distribution in China well. Over the past 58 years (1960–2017), ET_a and NPP have increased in the western regions and decreased in the eastern regions, with the boundary at 103° E. PUE presents a “low-high-low” spatial distribution from northwest to southeast in China. It is noteworthy that there was a zonal distribution for a high value area of PUE, which coincided with the summer monsoon transition zone. The soil moisture (SM) increase in arid regions is the main driving force of the PUE increase, whereas the annual net radiation (Rn) change in humid regions is the main driving force of the PUE change. The transition zone is the conversion zone, where the prevailing factor limiting vegetation growth transitions from water to energy.

The surface evapotranspiration (ET) process is the key link in the interaction between land and atmosphere. However, the influence of different environmental factors on ET over transitional climate zones and the physical pattern of the interaction between multiple factors remain unclear. Therefore, based on the continuous observation data during the vegetation growing season of a typical grassland in the Semi-Arid Climate and Environment Observation of Lanzhou University (SACOL) station from 2007 to 2012, the influence pattern of multiple environmental factors on ET over China's transitional climate zone was analysed. Each environmental factor exhibited significant seasonal and interannual variations. The mean value of ET was 1.67 mm day−1. Although the maximum values of sensible heat flux and vapour pressure deficit occurred in April and June, respectively, the maximum values of other environmental factors appeared from July to August. Net radiation, soil moisture, and normalized difference vegetation index (NDVI) were the main controlling factors of ET over the grassland, with correlation coefficients of 0.54, 0.52, and 0.46, respectively. The analysis of multiple environmental factors showed that when soil moisture, wind speed, net radiation, and NDVI reached 0.2 m3 m−3, 2 m s−1, 100 W m−2, and 0.2, respectively, ET varied in contrast with vapour pressure, vapour pressure deficit, and air temperature under the influences of weather processes, land–atmosphere coupling, and drought stress. These findings deepen our understanding of the role of ET in the land–atmosphere coupling process over the transitional climate zone in China.

The summer monsoon transition zone (SMTZ) in China represents an unusual land type with an agro-pasture ecotone, and it is a climate-sensitive region. Changes in climate aridity and changes in meteorological drought are mutually related yet fundamentally different. In this study, potential evapotranspiration (E_TO) is calculated using Penman–Monteith, based on China’s national meteorological stations data from 1961 to 2013. An E_TO-based climate aridity index (I_AI) and E_TO-based standardized precipitation evapotranspiration index (SPEI) are used as the metrics for climate aridity and meteorological drought, respectively. The result shows a significant difference between climate aridity and meteorological drought in the SMTZ, compared with the monsoon and non-monsoon zone. This difference varies on different time scales (1–48 months), and the greatest differences between I_AI and SPEI are on seasonal and monthly scales (1–12 months), but lower at longer time scales (>12 months). The first reason for the difference is the desynchronicity of meteorological drought and the background climate. After the background climate becomes a relatively arid state (such as 0.96 < I_AI < 1) from a semi-arid state (0.50 < I_AI < 0.80), the continued arid state with weak I_AI fluctuations eventually results in increasingly severe meteorological droughts, or the recurrence of equally severe droughts with drastic reduction. Consequently, the onset of the most severe climate aridity is two to seven months (mostly three to four months) ahead of the onset of the most severe drought events, until the climate returns to a semi-arid state. Second, climate aridity represents the average state of the background climate over a long time period and changes gently, while meteorological droughts are stochastic climate events and change drastically. These findings indicate that I_AI can serve as a predictor of the onset of meteorological drought events, especially in the SMTZ, but it fails to characterize the progression of meteorological drought events well. Therefore, this result is of great significance for drought prediction and early warning.