## 1 Introduction  
Drought, as a prolonged period of moisture deficiency in land surface, affects terrestrial ecosystems from structural and functional perspectives (i.e., constraining vegetation growth, causing plant mortality and triggering wildfire), which leads to profound imbalances in the terrestrial carbon cycle [1; 2; 3; 4]. In addition, climate change, which is a consequence of increased greenhouse gas emission and global warming [5; 6; 7], will exacerbate drought frequency and severity in the 21 st century [8; 9; 10; 11]. Drought is generally categorized to four types: Meteorological Drought, Agricultural Drought, Hydrological Drought and Socioeconomic Droughts. An agricultural drought indicates a deficit in soil moisture and thus it can happen in any land cover. An agricultural drought onset is typically perceived when the soil moisture level drops below a threshold causing crop water stress (affecting crop yield). Consequently, soil moisture is regarded as an indicator of agricultural drought [12; 13; 14]. Spatially varying precipitation, land cover, soil and topography cause heterogeneity, which makessoil moisture estimation from field measurement complicated [15; 16]. Therefore, land surface models and/or remotely sensed observations are often adopted to estimate soil moisture. There are many studies utilizing land surface models to estimate soil moisture and analyze historical agricultural drought episodes [17; 18; 19]. Additionally, remote sensing advances have provided major soil moisture data availability at global scale [20], which facilitates obtaining precise and frequent soil moisture maps globally [21; 22]. There are several studies, which compared agricultural drought analysis obtained from in situ and remotely sensed soil moisture data [23; 24; 25]. Some studies combined land surface models simulations with remotely sensed data to minimize the uncertainty of soil moisture estimation [26; 27]. Recent studies by [PERSON] et al. [28; 29] have assimilated remotely sensed soil moisture observations to land surface models in order to improve the accuracy of soil moisture simulations and drought monitoring. Ecosystem Water Use Efficiency (WUE) is defined as the ratio of carbon gain (i.e., Gross Primary Productivity; GPP) to water consumption (i.e., Evapotranspiration; ET), which links biological and water cycles over the land surface (WUE = GPP/ET) [30; 31]. GPP as a key component of the terrestrial carbon cycle, represents the sum of gross carbon (CO\({}_{2}\)) uptake by plant photosynthesis [32; 33]. Theoretically, the ecological transpiration is the true water consumption by plant photosynthesis. However, due to the infeasibility of distinguishing soil and canopy evaporation and plant transpiration from evapotranspiration (ET) [34], either precipitation [35] or ET [36] are used as indicators of water loss (i.e., consumed by the ecosystem). Among various definitions of WUE, the GPP/ET is the most common indicator and it is employed in this study too [1; 37; 3]. WUE is a key variable to understand the response of ecosystem productivity to any physical changes (e.g., water availability, climate change, etc.). Droughs can be associated with heatwaves and decreased water availability [38; 39], which may result in either increase or reduction of evapotranspiration, respectively, leading to significant disruptions in the global water balance and may cause permanent changes to the ecosystems [40; 41]. The WUE alteration and its effects on the ecosystem resilience to drought disturbances have been investigated in recent studies [1; 35; 42]. Regional assessments have concluded that ecosystem biomass are able to enhance their WUE in order to cope with water stress [36]. However, such a conclusion has been challenged by several large-scale investigations implying that the response of WUE to drought depends on the vegetation condition and climate [1; 30; 3]. Therefore, more investigation is still needed to understand WUE-drought relations and reveal the spatiotemporal patterns and influential factors.