# Multifaceted characteristics of dryland aridity changes in a warming world

Xu Lian, Shilong Piao, Anping Chen, Chris Huntingford, Bojie Fu, Laurent Z. X. Li, Jianping Huang, Justin Sheffield, Alexis M. Berg, Trevor F. Keenan, et al.

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Xu Lian, Shilong Piao, Anping Chen, Chris Huntingford, Bojie Fu, et al.. Multifaceted characteristics of dryland aridity changes in a warming world. Nature Reviews Earth & Environment, 2021, 2, pp.232-250. 10.1038/s43017-021-00144-0. insu-03726946

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Lian, Xu; Piao, Shilong; Chen, Anping; Huntingford, Chris ID; Fu, Bojie; Li, Laurent Z.X.; Huang, Jianping; Sheffield, Justin; Berg, Alexis M.; Keenan, Trevor F.; McVicar, Tim R.; Wada, Yoshihide; Wang, Xuhui; Wang, Tao; Yang, Yuting; Roderick, Michael L.. 2021. **Multifaceted characteristics of dryland aridity changes in a warming world. Nature Reviews Earth & Environment**, 2. 232-250. https://doi.org/10.1038/s43017-021-00144-0

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1

Multifaceted characteristics of dryland aridity changes in a warming world

2

Xu Lian¹, Shilong Piao¹,²,³,†, Anping Chen⁴,⁵,⁶, Chris Huntingford⁷, Bojie Fu⁸,⁹, Laurent Z. X.

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Li¹⁰, Jianping Huang¹¹, Justin Sheffield¹², Alexis M. Berg¹³, Trevor F. Keenan¹⁴,¹⁵, Tim R.

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McVicar¹⁶,¹⁷, Yoshihide Wada¹⁸, Xuhui Wang¹, Tao Wang², Yuting Yang¹⁹ and Michael L.

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Roderick¹⁷,²⁰

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¹ Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China.

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² Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China.

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³ Center for Excellence in Tibetan Earth Science, Chinese Academy of Sciences, Beijing, China.

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⁴ Department of Biology, Colorado State University, Fort Collins, CO, USA.

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⁵ Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA.

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⁶ Woodwell Climate Research Center, Falmouth, MA, USA.

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⁷ Centre for Ecology and Hydrology, Wallingford, UK.

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⁸ State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China.

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⁹ State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China.

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¹⁰ Laboratoire de Météorologie Dynamique, CNRS, Sorbonne Université, Ecole Normale Supérieure, Ecole Polytechnique, Paris, France.

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¹¹ Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China.

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23 ¹² Department of Geography, University of Southampton, Southampton, UK.

24 ¹³ Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA.

25 ¹⁴ Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.

27 ¹⁵ Department of Environmental Science Policy and Management, University of California Berkeley, Berkeley, CA, USA.

29 ¹⁶ CSIRO Land and Water, Canberra, ACT, Australia.

30 ¹⁷ Australian Research Council Centre of Excellence for Climate Extremes, Canberra, ACT, Australia.

32 ¹⁸ International Institute for Applied Systems Analysis, Laxenburg, Austria.

33 ¹⁹ State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China.

35 ²⁰ Research School of Earth Sciences, Australian National University, Canberra, ACT, Australia.

36 † email: slpiao@pku.edu.cn

37
38 Abstract

39 Drylands are an essential component of the Earth system and are among the most
40 vulnerable to climate change. In this Review, we synthesize observational and modelling
41 evidence to demonstrate emerging differences in dryland aridity dependent on the specific
42 metric considered. Although warming heightens vapour-pressure deficit and, thus,
43 atmospheric demand for water in both the observations and projections, these changes do
44 not wholly propagate to exacerbate soil moisture and runoff deficits. Moreover, counter-
45 intuitively, many arid ecosystems have exhibited significant greening and enhanced
46 vegetation productivity since the 1980s. Such divergence between atmospheric and
47 ecohydrological aridity changes can primarily be related to moisture limitations by dry
48 soils and plant physiological regulations of evapotranspiration under elevated CO2. The
49 latter process ameliorates water stress on plant growth and decelerates warming-
50 enhanced water losses from soils, while simultaneously warming and drying the near-
51 surface air. We place these climate-induced aridity changes in the context of exacerbated
52 water scarcity driven by rapidly increasing anthropogenic needs for freshwater to support
53 population growth and economic development. Under future warming, dryland
54 ecosystems might respond nonlinearly, caused by, for example, complex ecosystem-
55 hydrology-human interactions and increased mortality risks from drought and heat stress,
56 which is a foremost priority for future research.
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58 [H1] Introduction

59 Drylands describe regions subject to permanent or seasonal water deficiency, which currently 60 occupy ~42% of global land surface¹⁻⁴. They are typically located in subtropical regions 61 characterised by air mass divergence, in the rain shadow of mountain chains or in the middle 62 of continental landmasses⁵ (Fig. 1). Dryland ecosystems have a critical role in the global carbon 63 cycle, dominating the trend and variability of global terrestrial carbon sink owing to their high 64 sensitivity to inter-annual climate variability⁶. Moreover, they are home to ~30% of the world's 65 endangered and endemic species⁷,⁸, and are thus critical to global biodiversity conservation 66 efforts. Drylands also provide staple food, cotton, timbers and livestock to support nearly 2.5 67 billion people¹,³,⁸, among whom about half live below the United Nations poverty line. 68

68 The sustainability of ecosystem services and societal goods provided by global drylands, 69 however, is threatened by ongoing anthropogenic warming¹,²,⁹. Indeed, the limited socio- 70 economic capacity for adaptation and mitigation⁹,¹⁰, together with the faster-than-average 71 warming rate²,¹¹,¹², makes drylands among the most vulnerable regions to climate change. As 72 such, concerns about the fate of dryland socio-ecological systems have been a priority for 73 important global initiatives, including: The Global Dryland Ecosystem Programme¹³; the 74 Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services¹⁴; the 75 United Nations Convention on Biological Diversity¹⁵; the United Nations Sustainability 76 Development Goals¹⁶; and the Intergovernmental Panel on Climate Change³.

77 A surge of research has therefore emerged to assess dryland changes in the past and predict 78 their future trajectories, the findings of which are highly contradictory. For example, numerous 79 studies suggest that global drylands have become more arid¹⁰,¹⁷⁻²⁶, yet others show the same
regions experiencing greening and enhanced vegetation activity<sup>6,27-33</sup>. This apparent inconsistency stems from different interpretations of aridity – the state of insufficient water supply to meet demand<sup>34,35</sup> – dependent on whether it is used in an atmospheric, agricultural, hydrologic or ecological context (Box 1). The associated demand and supply sides of aridity, as well as the physical and/or biological processes driving changes of both sides, vary considerably among these different interpretations.

In this Review, we reconcile disagreements between aridity metrics by acknowledging the multifaceted nature of water supply and demand for the atmosphere, hydrological systems, ecosystems and human society over global drylands, as well as their interconnections<sup>35-37</sup>, knowledge of which will allow for more effective adaptation policies. We first provide an overview of how aridity and the extent of global drylands have changed since the 1950s and are projected to change in the future, presenting contrasting findings according to atmospheric, agricultural, hydrological or ecological dryland definitions. We next discuss the attribution of aridity changes to physical and physiological processes within the dryland system, arguing that strong soil moisture limitations, together with ecosystem physiological regulations of hydrological cycles under rising atmospheric CO<sub>2</sub>, can explain the apparent conflicting viewpoints. We then outline the central role of humans in water resource utilisation and evaluate dryland management policy decisions under the intensification of ecosystem-hydrology-human interactions. We end with a forward-looking perspective on future dryland research.

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[H1] Historical aridity changes in drylands
The growth of global data from remote sensing and in-situ networks, along with sophisticated climate-carbon cycle modelling, provides valuable new datasets and tools for assessing long-term surface aridity changes³⁸,³⁹ (Supplementary Table 1). A large body of research uses the aridity index, AI¹⁰,¹⁷,²¹,²²,²⁴,⁴⁰⁻⁴³, which represents the balance between water received by the land surface (precipitation) and that demanded by the atmosphere (potential evapotranspiration, PET) (Box 1). However, it has been suggested that AI could be problematic in depicting surface aridity changes, being too simplistic to capture the full complexity of aridity⁴⁰,⁴⁴,⁴⁵.

Hence, there is an increasing recognition that aridity should be evaluated using a more diverse representation of water demand and supply for different land surface processes³⁶,⁴⁰,⁴⁶. Four alternative aridity metrics have thus been increasingly employed, including: vapour pressure deficit (VPD), which measures the atmospheric water demand; soil moisture, which describes the soil water supply to support ecosystem function and agricultural production; runoff, which signifies the volume of freshwater available for drinking, irrigation, industry and other societal needs; and vegetation productivity and/or greenness, a robust indicator of ecological aridity given the primary role of water in determining dryland vegetation productivity²⁸.

With these five aridity metrics, as quantified by observations and state-of-the-art model simulations (12 offline dynamic global vegetation models, DGVMs, and 23 Earth system models, ESMs, from the Coupled Model Intercomparison Project Phase 5, CMIP5; Supplementary Tables 2 & 3), two approaches are used to assess dryland aridity changes since the 1950s and projections into the future (Fig. 2). The first takes zonal-average values of different aridity metrics over global drylands, while assuming a fixed global dryland extent (as in Fig. 1). The second calculates the evolving fraction of global water-stressed area, applying a
fixed threshold to each aridity metric to derive a temporally-variable dryland extent (Supplementary Methods; Supplementary Figs. 1-3). In the second approach, the fraction of global water-stressed area is denoted as $f_{atm}$, $f_{Al}$, $f_{soil}$, $f_{hyd}$, $f_{veg}$, for aridity metrics defined by VPD, Al, soil moisture, runoff and vegetation productivity, respectively. Aridity changes of global drylands using these different metrics are now quantified and compared.

## [H2] Ubiquitous atmospheric drying

VPD is defined as the difference between saturated water vapour pressure (which increases exponentially with air temperature) and actual water vapour pressure (which is a function of air humidity). Hence, VPD measures how far the air is from thermodynamic equilibrium, with high values related to strong atmospheric demand for water from land and/or water surfaces. Research into VPD changes consistently indicates an increasing trend of global dryland atmospheric aridity$^{47-49}$, as confirmed by various observational datasets (Supplementary Figure 4). On average, global dryland VPD shows a statistically significant increase of 0.012 ± 0.001 kPa decade$^{-1}$ ($p < 0.05$) over 1948-2016 (Fig. 2a; Table 1), as also reproduced by ESMs (0.015 ± 0.002 kPa decade$^{-1}$, $p < 0.05$). Consistent with the overall VPD increase, the global dryland area exposed to atmospheric water stress, as defined by VPD-based $f_{atm}$, similarly exhibits an increasing trend of 0.55 ± 0.11% decade$^{-1}$ ($p < 0.01$) over the same time period (Fig. 2f, Table 1). As dryland surfaces warm 20-40% faster than humid regions$^{41}$, the increase in VPD is amongst the highest values on the global land surface, ~25% higher than humid regions$^{48,49}$.
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[H2] Land surface drying characterised by the standard Aridity Index

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Traditionally, the most widely used metric to examine surface aridity was the AI<sup>10,21,24,42</sup>,
whereby a lower value corresponds to higher aridity, and is often interpreted as a higher risk of
desertification and land degradation<sup>3,10,50-52</sup>. Observational and modeling-based AI studies
generally point to an enhanced drying trend since the pre-industrial era, signifying an expansion
of dryland area (as defined by a standard threshold of AI ≤ 0.65)<sup>10,22,24</sup> **(Supplementary Table
1)**. For example, gauge-based precipitation and reanalysis-based PET data suggest a 2.4×10<sup>6</sup>

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km² expansion of global drylands when comparing 1991–2005 to the 1950s (ref. <sup>24</sup>), the strongest regional increases of which occurred in southern Africa, the Sahel and North China<sup>10,24</sup>. This surface drying trend is confirmed through analyses using multiple combinations of precipitation and PET datasets (**Fig. 2b**), translating into an ensemble mean global expansion of dryland area ($f_{AI}$) by 0.13 ± 0.06% decade<sup>-1</sup> (*p* < 0.05) over 1948-2016 (**Fig. 2g**, **Table 1**).

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However, the reported dryland aridity increase and dryland expansion are highly sensitive to selected inputs of observational data products<sup>38,53</sup>. For instance, the magnitude of the estimated expansion rate has a large spread across different data streams, ranging from near zero to 0.60% decade<sup>-1</sup> (**Table 1**). The previously reported rate of 0.57% decade<sup>-1</sup> (refs.<sup>10,24</sup>) falls into this range but is close to the upper boundary.

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While multi-data combinations provide a complete assessment of possible AI changes, they
cannot ensure the physical consistency between precipitation and other meteorological
variables involved in PET calculation<sup>53</sup>. Assessments with climatic diagnostics from ESMs
generally do not have this physical inconsistency. Indeed, the observed AI decrease and
± 0.03% decade⁻¹ (*p* < 0.05) during 1948-2005

(Fig. 2g, Table 1), consistent with the ensemble mean of multiple empirical data combinations (0.13 ± 0.06% decade⁻¹). For both observations and models, the expanding rate of global dryland extent based on AI is much slower than that based on VPD for the same period (Figs. 2f, g).

In addition, the AI is commonly calculated using the Penman-Monteith (PM) equation (Box 1), now thought to overestimate PET changes under elevated CO₂ because it incorrectly assumes a fixed resistance for vegetated surfaces⁴⁰,⁴⁵,⁵⁴,⁵⁵. The introduction of a CO₂-responsive surface resistance to the PM-based PET parameterization lowers PET increases (though it is still larger than concurrent precipitation increases⁴⁰), and the resultant AI shows higher consistency with modelled hydrological changes⁴⁵. Accounting for the CO₂ effect in the PET formulization thus leads to a ~40% reduction in the estimated increase in AI-based dryland extent during 1948-2016. As such, the resulting long-term trend becomes statistically insignificant at 0.08 ± 0.06% decade⁻¹ (Fig. 2g, Table 1). However, caution is needed when utilizing the modified PET model in dryland assessments as the model-based estimate of CO₂ effect on surface resistance has not been experimentally validated⁴⁵. With this limitation in mind, it is anticipated that the CO₂-modified PET model underestimates dryland PET increase and, thus, the rate of dryland expansion. Therefore, the rate of AI-based dryland expansion should fall between the estimates with and without accounting for the CO₂ impacts on PET, that is, between 0.08 and 0.13% decade⁻¹.

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[H2] Relatively weak total-column soil moisture drying

189 Soil moisture levels determine water stress for natural and agricultural ecosystems. Remote
190 sensing by microwave and gravimetric sensors, and its application in numerical land data
191 assimilation schemes, now offers a useful way to monitor soil moisture dynamics at large spatial
192 scales. However, there remain limitations with current technology including, limited vertical
193 sampling depth and low accuracy over densely vegetated surfaces for microwave-based
194 retrievals<sup>56,57</sup>. Hence, compared to the more robust finding of aridification and dryland
195 expansion based on VPD or AI, aridity changes based on soil moisture often show divergent
196 signs, depending on the data used, the period investigated, and the soil depth involved.

197 Using microwave satellite observations of near-surface soil moisture, it is estimated that 38.4%
198 of global drylands have experienced a significant drying trend since 1979, while only 2.9%
199 showed a wetting trend<sup>58</sup>. However, other observation-driven datasets of total-column (or root-
200 zone) soil moisture (such as GLEAM, TerraClimate and GLDAS, see **Supplementary Methods**) consistently reveal an increasing trend for the same period (Fig. 2c, Table 1). The
201 divergence of soil moisture trends is likely because surface and deep-layer soil moistures are
202 controlled by different processes—warming-induced rise in evaporative demand drives the
203 reduction of surface soil moisture, whereas soil moisture in deep layers is more controlled by
204 antecedent moisture status and vegetation activities<sup>59-61</sup>. Weaker drying in the root-zone
205 compared to the surface soil suggests that deep-rooted plants in drylands are less likely to suffer
206 severe soil moisture stress than shallow-rooted plants and crops.
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Over 2002-2016, gravimetric sensors onboard NASA's Gravity Recovery and Climate Experiment satellites, however, detect a robust decline of endorheic water storage²⁰ by about 106.3 Gt yr⁻¹, most of which is in drylands. Central Eurasia contributes most (69%) to this decline, followed by the Sahara Desert, the dry Andes, Australia and western U.S., while water gains were found in the Great Rift Valley and southern Africa²⁰. This signal of endorheic water loss reflects an overall reduction of soil moisture, surface water and groundwater levels, likely attributable to decadal or longer climate variability and human influence²⁰,⁶².

Despite strong model dependencies, soil moisture estimated by climate models also generally show a decreasing trend over drylands⁵⁹,⁶³,⁶⁴. The model-derived trend is qualitatively similar in pattern to the AI⁶³, and extends for both surface soils and the total soil column, although being more significant for the former⁵⁹,⁶⁰. As such, the global land area under (total-column) soil moisture stress predicted by both DGVMs and CMIP5 ESMs is increasing at 0.09 ± 0.05% decade⁻¹ (p = 0.05) and 0.05 ± 0.02% decade⁻¹ (p < 0.05), respectively over 1948-2016 (Fig. 2h, Table 1). The rate of dryland expansion inferred from modeled total soil moisture is substantially smaller than that from VPD or AI. Hence, the increased atmospheric evaporative demand has not fully translated to increased soil moisture deficits across all soil layers, despite its dominance on the enhanced soil moisture deficits at the near-surface layer⁵⁹,⁶⁰.

## [H2] Regionally divergent runoff changes

Along with deep groundwater resources, surface runoff is an important source of freshwater for societal demands of agricultural and industrial production in drylands. Unlike the ubiquitous
increase in surface aridity inferred from the VPD and AI metrics, runoff generally reveals divergent regional changes<sup>44,65,66</sup>, suggesting spatially heterogeneous hydro-climatic drivers and runoff responses.

Surface runoff of most dryland rivers is decreasing<sup>19,65,67-69</sup>. Indeed, through aggregating streamflow records of the world's largest rivers flowing through drylands (<strong>Supplementary Table 4</strong>), an overall declining trend in streamflow of -0.19 ± 0.12 mm yr<sup>-1</sup> (p = 0.11; or -11.9%) is apparent during 1948-2016 (<strong>Fig. 2d, Table 1</strong>). In general, regions experiencing rapid runoff decreases often overlap with those under intense human influences, such as the Yellow River in North China<sup>67</sup>, the Guadalquivir River in Spain, the Murray River in southeastern Australia and the Indus River in Pakistan<sup>70</sup> (<strong>Supplementary Table 4</strong>). Simulations by both DGVMs and CMIP5 ESMs agree with the observed overall decline (<strong>Fig. 2d, Table 1</strong>). Consequently, there is an increase of modeled land area subject to hydrological aridity evaluated by runoff deficits (f<sub>hyd</sub>), reaching 0.06 ± 0.07% decade<sup>-1</sup> (p > 0.10) for DVGMs and 0.03 ± 0.02% decade<sup>-1</sup> (p < 0.10) for ESMs (<strong>Fig. 2i, Table 1</strong>). Achieving a high statistical significance is, however, precluded by the strong decadal climate variability and spatial heterogeneity.

Nevertheless, increases in runoff have also been observed for about 20% of large rivers in arid regions<sup>65</sup>, such as the Daly River in north Australia and the Tana River in Kenya<sup>70</sup>. This runoff increase has been even more widespread since 1980s, driven by concurrent precipitation increases, as supported by both observational streamflow records<sup>70</sup> and a gridded synthesis runoff product<sup>71</sup> (<strong>Fig. 2i, Table 1</strong>).

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251 [H2] Reduced water stress for dryland vegetation

252 Increased VPD indicates a higher vapour-pressure gradient between leaves and the atmosphere,
253 enhancing water loss by plant transpiration⁴⁹,⁷². However, plants can also modify their water
254 need through physiological adjustments over both shorter and longer time scales, for example
255 through regulating stomatal conductance and changing leaf area, respectively¹⁹,³²,³⁷. In drylands,
256 how well plants adapt to changing hydrological regimes, and consequently how well they grow,
257 depends on whether the altered water supply is sufficient to meet their demand, which itself
258 could be evolving³⁷. Considering that water stress, owing to the insufficient water supply to
259 meet the demand, is the primary factor limiting dryland vegetation growth, the outcome of
260 vegetation changes provides a useful surrogate metric for evaluating ongoing water stress of
261 dryland vegetation.

262 Vegetation greenness indices from satellite observations (for example the Leaf Area Index, LAI;
263 Normalized Difference Vegetation Index, NDVI) consistently show a significant positive trend
264 in dryland foliage cover since the 1980s²⁸⁻³⁰, co-occurring with the enhanced atmospheric
265 aridity indicated by VPD and AI. This enhanced dryland vegetation growth has remained strong
266 into the 21st century, as confirmed by greenness indices from the well-calibrated MODIS
267 collection 6 (Fig. 2e) and SPOT satellite data³²,⁷³. Some regional hotspots of vegetation
268 greening also overlap with drylands under intensive agricultural expansion, such as in India,
269 the North China Plain, the U.S. Great Plains and southeast Australia³²,⁷³.

270 Furthermore, aboveground production of dryland ecosystems, based on both empirical
271 observation-based product and process-based models, also presents an increasing trend that
272 accounts for over 50% of the increase in global carbon sink during 1982-2011 (ref. ⁶) (Fig. 2e,
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**Table 1**). Dryland gross primary production (GPP) simulated by DGVMs and ESMs also increases at a rate of 0.6 PgC decade⁻¹ and 1.0 PgC decade⁻¹, respectively. Such changes correspond to an overall reduction in the areal coverage of vegetation growth-defined drylands (*f*<sub>veg</sub>) by 4.8% (for DGVMs) and 5.5% (for ESMs) over 1948-2005 (Figs. 2j, Table 1), in contrast to the areal increases implied by *f*<sub>VPD</sub> and *f*<sub>AI</sub>.

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Thus, a holistic overview of changes in surface aridity and dryland extent suggests that aridity changes involve multi-dimensional land surface responses to climate change, which could not be captured by any single offline aridity metric. This fact is particularly critical for dryland assessment, as different indices point to highly divergent aridity changes and rates of dryland expansion in recent decades. Specifically, dryland soil moisture and surface runoff decrease following near-surface atmospheric drying (based on VPD), albeit with substantial uncertainties and regional heterogeneity. In contrast, satellite records and models consistently illustrate a greening and more productive trend in the warmer and CO₂-enriched drylands.

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## [H1] Future aridity changes in drylands

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With atmospheric and land surface drying observed since around the 1950s, there is clear interest in understanding how dryland aridity conditions will change with future warming. Indeed, given that water resource shortage is the major constraint to socioeconomic growth and ecological security in drylands, understanding future aridity changes is critical to regionally targeted adaptation planning and decision making<sup>17,41</sup>. ESMs provide sufficient climate and land surface diagnostics to allow for systematic assessment of future aridity metrics, and have
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been widely tested and used¹⁰,¹⁷,²⁴,⁴⁰,⁶³. To better understand dryland projections under various

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temperature thresholds, we quantify the percentage change in dryland area for different aridity

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metrics ($f_{atm}$, $f_{AI}$, $f_{soil}$, $f_{hyd}$ and $f_{veg}$) in different regions using CMIP5 ESM projections (Fig. 3).

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[H2] CMIP5-projected changes

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AI-based aridity projections with CMIP5 models estimate a persistent increase of global

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dryland extent by ~1-4% depending on temperature threshold used (Figs. 2g, 3f). These changes

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are broadly consistent with previous analyses which suggest a 4% and 10% increase of global

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dryland extent by 2100 relative to 1961-1990 under RCP4.5 (an intermediate emission scenario)

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and RCP8.5 (a high-end emission scenario), respectively²⁴.

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warming is over 1.5°C above pre-industrial, the signal of increased aridity can be distinguished

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from natural variability for 8% and 24% of the global land surface for the two scenarios; for

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2°C warming, these values increase to 10% for RCP4.5 and 32% for RCP8.5 (ref. ¹⁷). However,

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this expansion is not spatially uniform; for example, up to 10% increases in dryland extent are

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projected for southern Africa with 2°C warming, whereas a reduction of ~2% is anticipated for

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northern Africa (Fig. 3f).

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Global drylands are commonly divided into four subcategories (hyper-arid, arid, semi-arid and

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dry sub-humid) based on a range of AI thresholds¹⁰ (Box 1). Among these four subcategories,

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semi-arid and arid regions are estimated to expand in area by 1-15% and 1-5%, respectively,

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under 2°C warming (Fig. 3g-j). The expansion of semi-arid regions mainly occurs in the

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Mediterranean, southern Africa and North and South Americas, while the expansion of arid
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regions occurs in southwestern Africa²⁴, predominately by encroaching into neighbouring less
arid zones (Fig 3g-j). The spatial extent of dry sub-humid regions, in contrast, is projected to
slightly decrease through fast converting to drier climates (Fig. 3g). When accounting for CO₂
impacts on surface hydraulic resistance in the definition of AI ($f_{AI\_CO₂}$), a qualitatively similar
expansion of global drylands is found, although the expansion rate is lower for almost all the
continents (Fig. 3f), as also evident in the present climate.

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For alternative interpretations of dryland extent, projected changes generally exhibit similarities
in sign to those found for the historical period. Specifically, under RCP4.5, ESMs project that
historical trends of strongly increasing $f_{atm}$, slightly increasing $f_{soil}$, and strongly decreasing $f_{veg}$
will continue to the end of the 21st century (Fig. 2). In more detail, $f_{atm}$ is anticipated to
experience rapid expansion across all the continents, exceeding 10% of total area when
warming is higher than 2°C (Fig. 3a). This widespread atmospheric drying trend is also
evidenced by the progressive expansion of more arid dryland subcategories into previously less
arid regions (Fig. 3b-e). Similarly, $f_{soil}$ is also projected to increase throughout the 21st century
across all the continents except for northern Africa (Fig. 3k), with the largest fractional increase
occurring in semi-arid regions (Fig. 3l-o). One notable change is that although $f_{hyd}$ historically
shows a slight increasing trend, this metric is projected to be reversed to a future decreasing
trend under RCP4.5 (Fig. 2i, Table 1). However, the projected future $f_{hyd}$ change is regionally
heterogeneous, with slight expansions anticipated in North America, South America and
southern Africa, but slight reductions in Eurasia, Australia and northern Africa (Fig. 3p).

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Following a similar trajectory to historical changes, the projected future $f_{veg}$ shows a widespread
and persistent decreasing trend (Figs. 2j, 3u), spanning from -6.6% in southern Africa to -25.3%

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± 3.3% globally relative to the 1961-1990 baseline (Fig. 3u). Vegetation in many of the historical transitional zones between dry and wet climates (defined as sub-humid or semi-arid), such as in temperate central North America and northeastern China, is expected to move from arid-climate-adapted to humid-climate-adapted (Fig. 3z). All the four subcategories of dryland vegetation are expected to decrease in area, except for semi-arid regions in Australia and Eurasia where more arid areas are moving into this vegetation-defined semi-arid subcategory than those moving out of semi-arid areas to dry sub-humid due to the stronger greening trend in arid ecosystems (Fig 3v-y).

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## [H2] CMIP6-projected changes

Adding to the well-established CMIP5 ensembles of simulations, newer simulations archived in the Phase 6 of the Coupled Model Intercomparison Project (CMIP6)⁷⁴ offer higher spatial resolution, improved physical parameterisations and the inclusion of additional Earth system processes⁷⁴,⁷⁵.

Under SSP2-RCP4.5 – a scenario of intermediate emission and continuing historical socioeconomic activities – CMIP6 ESMs project a persistent expansion of AI-based drylands over the 21st century at an average rate of 0.28 ± 0.02% decade⁻¹ (*p* < 0.05) driven by a positive VPD trend of 0.030 ± 0.001 kPa decade⁻¹ (Supplementary Table 5; Supplementary Fig. 5). Owing to the greater growth of atmospheric CO₂ forcing in SSP2-RCP4.5, this rate of dryland expansion is larger than the 0.20 ± 0.02% decade⁻¹ (*p* < 0.01) projected by CMIP5 under RCP4.5

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359 When examining hydrological changes, CMIP6 ESMs show a significant increase in both total-column soil moisture and surface runoff over 2015-2100: 0.05 ± 0.01% decade⁻¹ (*p* < 0.01) and 0.03 ± 0.01 mm yr⁻¹ (*p* < 0.01), respectively (Supplementary Fig. 5c, d). Despite the overall surface wetting, changes are spatially heterogeneous and regional hotspots of strong drying occur in the Mediterranean, western North America, southern Africa and Australia⁶⁰. The overall wetting projected by CMIP6 ESMs is in contrast to the overall drying (or insignificant change) projected by CMIP5 (Fig. 2c, d), which might be attributed to precipitation enhancements in the Middle East and northern Africa⁶⁰ and higher surface resistance under elevated CO₂ (ref ⁴⁵) in the former. A caveat is that such CO₂ regulation of hydrological dynamics has not been explicitly considered in some CMIP models because they represent a class of Atmosphere-Ocean General Circulation Model (AOGCM) without simulating dynamic vegetation. Hence, uncertainties exist in previous CMIP-based assessments of future hydrological changes as AOGCMs are commonly included⁴⁵,⁶⁰,⁶³. For dryland ecosystem responses, CMIP6 ESMs also project that the historical trends of ecosystem greening and enhanced productivity will continue into the near future, similar to CMIP5 ESMs.

374 Model projections from CMIP5 therefore broadly show a persistence of contemporary trends 375 of dryland aridity changes throughout the 21st century. In particular, despite the continuously 376 rising atmospheric dryness and soil moisture stress, dryland ecosystems are expected to 377 continue greening. The divergent aridity changes over drylands with CMIP5 ESMs are also 378 predicted by recently released CMIP6 simulations; however, these project a stronger 379 atmospheric drying based on VPD and AI, and a robust trend of land surface wetting (rather
than drying or slight wetting based on CMIP5) assessed with soil moisture and runoff.

381

[H1] Mechanisms for dryland aridity changes

It is generally known that many land and atmospheric aridity processes, such as VPD, soil moisture and vegetation greenness, are essentially coupled over sub-seasonal to inter-annual timescales<sup>47,48,76</sup> (Fig. 4a). However, over multi-decadal or longer timescales, highly divergent—and in some instances decoupled—trends of dryland aridity changes emerge, the size and direction of which are dependent on the chosen aridity metric. Specifically, as discussed, atmospheric aridity, characterised by water vapour and precipitation deficits, does not fully propagate to water deficits in the hydrosphere and the biosphere. This phenomenon is likely because each component of the coupled atmosphere-plant-hydrology system has a distinct and interactive response to rising atmospheric CO<sub>2</sub> and associated effects (Fig. 4a), and thus reveals different aspects of dryland aridity changes. In particular, accounting for the soil moisture constraints and leaf stomatal physiological response to rising CO<sub>2</sub> helps explain and reconcile the divergence in aridity changes by different metrics to some extent, as now discussed.

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[H2] Thermodynamic changes drive higher atmospheric demand for water

Observed atmospheric drying – that is, rising VPD (Fig. 2a,f) – is generally accepted as a simple thermodynamic consequence of warming<sup>18,36</sup>. The lack of surface moisture and evaporative cooling amplifies regional surface warming in drylands compared to humid lands and oceans<sup>2</sup>.

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The higher temperatures augment the capacity of the air to hold more water vapour. However, the greater warming over land than oceans impedes the transport of moist air masses from oceans to the land, particularly in subtropical subsidence zones, making the increase of near-surface specific humidity over the land relatively small and insufficient to follow Clausius-Clapeyron scaling (~7% °C)¹⁸,⁷⁷,⁷⁸. The enlarged contrast between actual and saturated water vapour, that is, higher VPD, provides an explanation for the PET increase¹⁸,²⁴,³⁶, which exceeds concurrent precipitation increases and thus causes reduced AI values (enhanced aridity) and the associated expansion of dryland extent¹⁰,¹⁸,²⁴,²⁵ (Figs. 2b, 2g, 3f).

Furthermore, land surface processes also modulate atmospheric dryness though land-atmospheric feedbacks⁴⁸,⁷⁶ (Fig. 4a). For instance, warming-induced depletion of soil moisture attenuates evaporative cooling and elevates temperature (thus also elevating VPD and PET), while simultaneously enhancing atmospheric stability and inhibiting cloud formation and precipitation generation⁷⁶. Soil moisture depletion also strengthens the gradients of air humidity and pressure between drylands and surrounding humid regions, which promotes low-level moisture convergence and increases precipitation transported to global drylands⁷⁹. Jointly, these compensatory physical processes lead to an exacerbation of surface aridity characterized by decreased AI values⁷⁶.

Importantly, the explanation of dryland aridity changes with atmospheric thermodynamics and regional land-atmosphere interactions does not account for structural changes of large-scale atmospheric circulations. In particular, the Hadley Circulation is essential for the development of subtropical dryland climates². Observational evidence has indicated a widening (or poleward expansion) of the subsiding branches of the Hadley Circulation, as well as a strengthening of
descending motion in the subtropics⁸⁰,⁸¹. These circulation changes suppress convective
precipitation and reduce tropospheric humidity, hence intensifying surface aridity in subtropical
zones⁸⁰,⁸¹. However, uncertainties remain in changes of the Hadley Circulation and associated
climatic consequences, and they are not able to explain the observed overall increase in
precipitation over subtropical lands². In addition, climate model projections of drier subtropics
under future warmer climates contradict paleoclimatic evidence of wetter subtropics in past
warm periods⁸²; the wetter subtropics under past warm climates are suggested to co-occur with
weakened (rather than present-day strengthened) subtropical subsidence of the Hadley
circulation associated with weaker-than-present meridional sea-surface temperature gradients⁸².

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### [H2] Vegetation physiological responses to rising atmospheric CO₂

Assuming fixed or only small increases in precipitation, alongside no change in direct human
consumption, available water resources (soil moisture and runoff) over the land will decrease.
This reduction arises from anthropogenic warming-enhanced evaporative losses owing to
heightened PET¹⁸,⁶⁸,⁷²,⁸³. However, changes in actual evapotranspiration (AET) are governed
by more than the thermodynamics which determine PET. In particular, the thermodynamically
driven growth in PET cannot translate into similar growth in AET over drylands, as the limited
soil moisture supply strongly constrains the AET increase if precipitation remains steady⁸⁴,⁸⁵
(Fig. 4b). There is also growing evidence that vegetation physiological processes have a critical
role in controlling AET and thus other hydrological fluxes⁴⁵,⁸⁶⁻⁸⁹. The different governing
processes of PET and AET create a growing gap between them under rising atmospheric CO₂,
which underlies the inconsistent changes between AI (depending on PET changes) and other

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445 ecohydrological aridity measures (affected by AET by varying amounts) (Fig. 4b).

446 As atmospheric CO₂ increases, plant leaves partially close their stomata and reduce the amount
447 of water loss through stomatal transpiration. This leaf-scale stomatal regulation is simultaneous
448 with, and could be potentially counteracted by, changes in structural and functional attributes
449 of plants, including enhanced photosynthesis⁹⁰, increased canopy leaf area³² and deepened
450 rooting system⁹¹ (Fig. 4a). In particular, the expansion of foliage area induces extra water loss
451 via leaf transpiration and canopy evaporation of intercepted rainfall, while simultaneously
452 decreasing evaporative water loss from the soil surface³²,⁹². As plant transpiration accounts for
453 more than half of the land AET in dryland natural ecosystems⁹³, transpiration (rather than
454 evaporation) responses to higher CO₂ will likely dominate the overall dryland AET changes in
455 a CO₂-enriched world⁹⁴.

456 However, at least for the recent past (1948-2016), there is no consensus as to the net
457 hydrological effect of plant physiological responses to higher CO₂, linked to uncertainties in
458 the complex interactions and compensations among relevant biophysical processes¹⁹,⁹⁵. Under
459 future scenarios with a much higher atmospheric CO₂ concentration (for instance, reaching a
460 quadrupling of the pre-industrial CO₂ level), model projections generally agree on a net
461 decrease of canopy-level transpiration and land AET after accounting for both stomatal
462 regulation and foliage expansion⁴⁵,⁸⁶,⁸⁷,⁸⁹. This physiological forcing of high atmospheric CO₂
463 on AET could even dominate over its radiative forcing in some less-arid dryland areas with
464 sizable vegetation cover⁸⁷.

465
The plant physiological responses to elevated CO₂ can partly explain the divergent dryland aridity changes, simultaneously ameliorating ecohydrological water stress and increasing atmospheric dryness (Supplementary Figure 6). The stomatal regulation of transpiration under elevated CO₂ level can conserve water, which generally favors the partitioning of precipitation towards runoff and soil moisture<sup>45,86-88,94,96</sup> (Figs. 4, Supplementary Figure 6). Vegetation growth of water-limited biomes, as for drylands, is more sensitive to soil moisture deficit than to atmospheric moisture deficit<sup>97,98</sup>, and hence CO₂-driven water saving can offset the higher water demand driven by a warmer atmosphere. Such CO₂-driven water-saving is sufficient to ameliorate water limitations for photosynthesis in semi-arid grasslands, as confirmed by manipulative free-air CO₂-enrichment experiments<sup>99-101</sup> and factorial simulations with DGVMs (Box 2). This water-saving mechanism, in concert with CO₂-induced stimulation of photosynthesis, longer growing seasons and attenuated soil moisture stress at places with increased precipitation, translates to extra carbon gain and enhanced growth and moves the dryland ecosystem to a new hydro-ecological equilibrium<sup>28,102,103</sup>.

Meanwhile, the physiologically induced transpiration decrease also feeds back to surface climate through reduced evaporative cooling, thus contributing to the warming<sup>104</sup> and relative drying of the near-surface air, which is reflected in enhanced VPD<sup>76,89</sup> (Supplementary Figure 6). The CO₂ physiological effect on hydrology and surface climate, as previously discussed, is small in extremely arid areas, but in semi-arid and sub-humid areas, this effect is substantially larger and even comparable to that in humid areas (Supplementary Figure 6).

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[H2] Uncertainties in current understanding of CO₂ physiological impacts
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Large uncertainties exist in the estimation of CO₂ physiological impacts on dryland ecohydrological changes. Many studies of CO₂ physiological forcing are based only on numerical simulations that depend heavily on model parameterisation schemes of ecosystem response<sup>45,86-89,105</sup>. For example, some experimental studies suggest that the capacity of dryland plants to optimize their carbon sequestration and water utilization under higher CO₂ is limited owing to the dominant role of soil moisture limitations in controlling plant physiological responses to CO₂ (refs.<sup>106,107</sup>) and possible nutrient depletion<sup>108</sup>. Such processes are not yet sufficiently understood, and consequently errors in their parameterisation in DGVMs might be substantial<sup>32</sup>. At present, more observational and experimental techniques, such as leaf gas exchange, stable isotope discrimination and eddy covariance measurement, are now implemented to investigate plant water use<sup>109-111</sup>. Yet, these approaches are rarely applied to the scale of ecosystem or river basin, due to the substantial discrepancy at different spatial and temporal scales<sup>109</sup> or the short temporal coverage that precludes the detection of slowly evolving CO₂ impacts.

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ESMs project that dryland vegetation greening will continue to benefit from future atmospheric CO₂ enrichment (refs. <sup>32,103</sup>). However, as temperature continues to increase, whether the positive CO₂ physiological impact on vegetation will persist remains an open question. Temperature and precipitation feedbacks from CO₂-induced stomatal closure and AET reduction can amplify the risk of surface heat stress<sup>112-114</sup>, which might offset, or even reverse, the positive effect of water stress relief. More importantly, with increasing warming, plants could need to keep stomata open to cool their leaves from irreversible heat damage, despite elevated CO₂ allowing for reduced stomatal aperture to maintain the same or even raised

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510 photosynthetic rate<sup>115,116</sup>. This required stomatal opening could be increasingly important for
511 dryland biomes as temperatures rise, since current temperatures appear to be near or above the
512 optimum for photosynthesis<sup>117</sup>. Future research needs to place more emphasis on understanding
513 possible nonlinearities or tipping points of critical transitions<sup>50,51</sup> in dryland ecosystem
514 responses to future global warming, and improving their representations in process-based
515 models. In particular, long-term manipulative field experiments will be useful to better
516 characterise any nonlinear features of plant responses and assess dangerous levels of future
517 warming over drylands<sup>118,119</sup>.

518

519 [H1] Towards a human-dominated dryland

520 Earth is now in the Anthropocene era<sup>120</sup> when humans are playing a central or even dominant
521 role in shaping terrestrial greenness patterns and modifying regional hydrological cycles<sup>62,121-123</sup>. Direct human impacts are, in some instances, more substantial than natural factors and
522 indirect human forcing via climate change. Global drylands are currently experiencing the
523 fastest population growth in the world<sup>124</sup>, which makes the already fragile dryland environment
524 even more vulnerable to water scarcity. Therefore, human freshwater use, which is heavily
525 influenced by demographical, social, economic and technological factors<sup>125</sup>, must be
526 incorporated in dryland aridity assessment and predictions. Global hydrological models
527 (GHMs), forced by varying scenarios of both historical and future socio-economic factors and
528 greenhouse-gas emissions (<strong>Supplementary Methods</strong>), are available for such assessments<sup>126,127</sup>
529 (<strong>Fig. 5</strong>).
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531 GHMs indicate that since the 1950s, rapid population growth and economic development has
532 increased human water demand in drylands by approximately 200% (Fig. 5g). Furthermore,
533 this rapid increase in human water demand is expected to increase by ~270% (on the baseline
534 of 1950s) by 2090s under SSP2-RCP6.0, a no-mitigation emission pathway (Fig. 5g).
535 Agricultural irrigation contributes the greatest overall increase of dryland water use for all the
536 continents, for both historical and future periods¹²⁸,¹²⁹ (Fig. 5). In addition, industrial and
537 domestic water demands are also projected to increase substantially, especially in African
538 drylands (Fig. 5b, e).

539 However, GHMs forced with changing socio-economic factors do not project monotonically
540 increasing human water demand throughout the 21st Century (Fig. 5g). These models estimate
541 an increase in water demand consistently for all the continents before the 2030s. After the
542 2030s, however, regional projections diverge, with a continuous rise in Africa (Fig. 5b, e), a
543 levelling off in Eurasia and South America (Fig. 5c, d) and a decline in North America and
544 Australia (Fig. 5a, f). Given the relatively small water supply changes of surface runoff, the
545 rapid augmentation of water needs in Eurasia, South America and in particular Africa will
546 exacerbate societal water scarcity (the gap between water demand and supply) in those regions
547 (Fig. 5). This geographical divergence also highlights a major concern that many of the poorest
548 regions, with very limited access to financial and technological resources, are more likely to be
549 exposed to high pressures of water scarcity in the future. Importantly, there is accumulating
550 experimental evidence that crop plants can use water more efficiently under rising atmospheric
551 CO₂ (refs. ¹³⁰⁻¹³²). The CO₂ physiological effects can partly, though not fully, offset the
552 anticipated increase of agricultural water consumption, and thus alleviate the adverse impact of
553 surface warming and drying to some extent¹³⁰-¹³².

554 The rapidly growing freshwater demands might not be sustainable, and have already imprinted
555 noticeable footprints on local to global hydrological cycles. Agricultural expansion and
556 intensification, though contributing to regional greening and increased vegetation productivity
557 in many places³²,⁷³, are often accompanied with excessive withdrawal of surface waters (mainly
558 runoff) and over-extraction of groundwater storage¹²⁸,¹³³,¹³⁴. In particular, gravity-based remote
559 sensing demonstrates that groundwater levels in India¹³⁵, the North China Plain¹³⁶ and western
560 U.S.⁸³ are falling at an alarming rate. Groundwater extraction for agricultural irrigation is now
561 exceeding the natural recharge rate in these regions, recognised as the main cause for the
562 observed decline in groundwater levels¹²⁸,¹³⁴,¹³⁵. Many plant species in arid and semi-arid areas
563 depend on shallow groundwater for survival, particularly during dry seasons when other water
564 sources are largely unavailable¹³⁷,¹³⁸. Therefore, such unsustainable groundwater depletion also
565 poses a significant threat to the health of dryland natural ecosystems¹³⁸,¹³⁹.

566 In addition to the direct extraction of water resources, other human land-use management
567 practices, such as afforestation (or reforestation), deforestation, overgrazing and urbanization,
568 also leave remarkable imprints on dryland ecohydrological systems. For example, while
569 contributing to halting desertification and increasing carbon storage¹⁴⁰,¹⁴¹, large-scale
570 ecological restoration programmes implemented in semi-arid or sub-humid North and West
571 China have caused extra evaporative water loss and a related significant decrease in regional
572 runoff and terrestrial water storage¹⁴⁰,¹⁴²,¹⁴³. The faster return of water to the atmosphere means
573 that less water is available for other socio-economic needs. Meanwhile, overgrazing is also a
574 significant anthropogenic factor contributing to grassland deterioration and even the shift of
dominant vegetation types, potentially disturbing local hydrological cycles. This deterioration is a key environmental problem faced by regions economically dependent on livestock, such as Mongolia, central Eurasia, Latin America and Sub-Saharan Africa<sup>144,145</sup>. By removing protective plant cover and by livestock trampling, overgrazing may cause the compaction of soils, reducing infiltration and accelerating runoff and soil erosion<sup>146</sup>.

More efficient water-resource management measures can help to cope with the increasing water crisis, with substantial co-benefits for the sustainability of the coupled dryland social-ecological system. First, taking advantage of the increasing crop water-use efficiency under elevated CO₂ (refs. ¹³¹,¹³²), it might be possible to gradually reduce irrigation water usage per unit area of agricultural land. This irrigation reduction is complementary to other agricultural water management approaches, such as rainwater harvesting, precise irrigation, improved irrigation infrastructure. Second, there is growing attention placed on the benefits of future forestry practices, and especially afforestation as a potential solution to partially offset CO₂ emissions¹⁴⁷,¹⁴⁸. Afforestation programmes in dryland regions need to use locally adapted and water-efficient indigenous species to avoid unnecessary evaporative water losses¹⁴⁹. Careful evaluation is also required of the land carrying capacity to support anticipated plant growth¹⁴⁰,¹⁵⁰. Third, policy decisions may gain from incorporating water-relevant ecological information from the ever-expanding near real-time data streams from space. For example, the incorporation of satellite-monitoring of GPP and chlorophyll fluorescence into drought early-warning systems¹⁵¹ can help guide governments to take anticipatory mitigation actions in preserving farming livelihoods and food security.

New water-conservation technologies are also helpful to safeguard dryland water and food
security. For example, improved agricultural biotechnology is expected to breed more drought and/or salt-tolerant, water-efficient and more productive cereal and forage crops¹⁵². Such advances will simultaneously increase agricultural production and reduce water usage.

Modelling evidence also suggests that the adoption of new technologies, such as developing renewable energy and recirculating dry-cooling systems in electricity generation, could lower ~32% of global water requirements by 2100 under the SSP5 scenario (ref. ¹⁵³). Nonetheless, the effectiveness of such technological innovations for drylands depends on their transfer to and implementation by some of the world's poorest countries.

605

## [H1] Summary and future perspectives

Dryland ecosystems are a pivotal part of the Earth system and their sustainability is critical both locally and for our shared societal future. While water is the central component in defining dryland social-ecological systems, aridity in drylands is tightly interconnected with climate, vegetation and humans, all of which are vary over different spatio-temporal scales. Whilst numerous studies define dryland changes with a broad range of metrics, these often generate highly conflicting conclusions. Such discrepancies lead to less trust in projections of environmental change and can preclude the accurate determination of dangerous thresholds of climate alteration.

This review leverages comprehensive strands of data and research pertaining to drylands for a holistic overview of their global changes. By viewing the emerging aridity changes as adjusting attributes of a coupled atmosphere-ecohydrology-human system, we provide a cohesive picture
618

of recent, ongoing and future dryland changes (Fig. 6). At short timescales such as daily to
inter-annual, aridity changes are primarily governed by climate variability regardless of the
metric used. Yet, over longer decade-to-century timescales, the CO₂ physiological effect is
likely a more important driver for vegetation-related aridity changes, with important feedbacks
to local and regional hydrological cycles (Fig. 6). The role of plant physiological mechanisms
in ameliorating plant water stress and fostering plant growth will become even more important
and helpful, against a backdrop of rising water and food demands by the rapidly growing
population (Fig. 6).

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Although the mechanistic understanding of surface aridity changes is for the recent past and the
near future, it might also have broader implications beyond the contemporary timescale. In
particular, the CO₂ physiological forcing provides a useful mechanism for explaining the
warmer-and-greener association observed over the geological timescale. Ice-core and pollen-
based hydroclimate reconstructions suggest that warmer inter-glacial periods (for example, the
Pliocene period with atmospheric CO₂ concentration of 350–450 ppm, as comparable to the
current CO₂ level) are often associated with lower dust levels (often indicative of a wetter land
surface)¹⁵⁴ and generally higher vegetation cover, compared to colder glacial periods (for
example, the Last Glacial Maximum with atmospheric CO₂ concentration of ~190 ppm)¹⁵⁵⁻¹⁵⁷.
This phenomenon again contradicts with model-estimated lower AI values (land surface drying)
for warmer periods¹⁵⁸, but does agree with present-day trends for increasing vegetation cover.
That is, analysis from the current climate suggesting that the physiological influence of higher
CO₂ levels in warmer periods shapes the surface water cycle and prevents the expansion of arid
and semi-arid ecosystems, and this present-day process has presumably operated throughout
the geological timescale. Decreased dust deposition in warmer periods does not, however,
necessarily imply a wetter land surface owing to potential confounding factors such as changes
in large-scale circulation patterns<sup>82</sup>, wind speed, aerosol concentrations, and the likelihood that
less dusty air is itself a result of higher vegetation cover<sup>63,154</sup>.

Looking forward, we identify several remaining knowledge gaps as priorities for future research
and policy decisions. Aridity, calculated as a long-term but changing average state of water
scarcity, cannot represent all aspects of the societally relevant hydrological responses expected
for future climatic states. Future research needs to prioritize the understanding of possible
enhanced risk posed by short-term hydro-climatic anomalies and how ecosystems subsequently
respond and adapt to such climate perturbations.

As the climate gets warmer, there is substantial evidence of increasing chances of more frequent
and severe climate extremes, particularly droughts<sup>107,159-161</sup>. Extreme dry events can trigger
detrimental damage to the fragile dryland ecosystems, including raised mortality levels and
enhanced fire risks<sup>162-164</sup>. As one of the most fire-prone ecosystems, drylands account for more
than 80% of global wildfires, with massive losses of plant biomass and soil nutrients<sup>165,166</sup>.
Observational evidence shows widespread woody encroachment in subtropical savannas
associated with decreased burned areas and fire return intervals during 1997-2016 (ref. <sup>167</sup>).
Contrary to the observed downward trends of historical records, current fire models usually
project rising risks of fire disturbances under future climate change, yet with a large spread of
probabilities<sup>166,168</sup>. The impact of future shifting fire regimes on dryland ecosystem-hydrology
changes is thus still largely uncertain. However, dryland vegetation is also highly resilient and
can often recover quickly from past disturbances<sup>169</sup>, despite possible increasing risks from
droughts and fires. The high resilience and fast recovery might represent a key mechanism for the adaptation of dryland vegetation to past and future climate change, and can additionally contribute to the differences noted between changes of vegetation aridity and those of other aridity indicators.

Global drylands also encompass a diverse range of regions, cultures and ecosystems, resulting in regional divergence in their aridity changes and associated impacts. This divergence implies highly regionalised challenges in meeting the societal and ecosystem needs of water resources³.

Targeted regional assessments of dryland aridity changes will better inform effective mitigation actions if tailored to different specific localised needs.

There additionally remain challenges to observe and model accurately long-term changes of surface aridity and water resources¹⁵⁹,¹⁷⁰,¹⁷¹. Field measurements and experiments remain scarce in drylands, and at present, datasets are often too short to provide definitive answers on the long-term effects of CO₂-fertilization and stomatal behaviour. The scarcity of data prevents characterising any potential nonlinear responses as atmospheric CO₂ concentrations rise even higher. Thus, there is a pressing need for future efforts to build more extensive and high-quality dryland observation networks that operate over a range of spatial scales and substantial periods of time. Yet, even for measurements that are available, there need to be more far-reaching integrated analyses of emerging space and ground-based measurements, such as eddy covariance, FACE experiments, plant functional traits and tree-ring chronologies, to provide a more complete mechanistic understanding of ongoing land surface processes. Moreover, future hydrological models must treat vegetation as a dynamic component, and explicitly consider the feedbacks of its structural and physiological changes to other key water-cycle components.
Current models generate substantial uncertainties in projected trajectories of dryland water-resource availability and requirements. Constraining such uncertainties requires refining the representation of the complex interactions between the climate, hydrology, ecosystems and humans. An improved predictive capability will support policymaking to achieve sustainable management of global drylands, to better service different societal and ecosystem needs in a warmer and CO₂-enriched world.
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1110 180 Donohue, R. J., Roderick, M. L., McVicar, T. R. & Yang, Y. A simple hypothesis of how leaf and canopy-level transpiration and assimilation respond to elevated CO₂ reveals distinct response patterns between disturbed and undisturbed vegetation. *J. Geophys. Res.* 1112 *Biogeosciences* **122**, 168-184 (2017).

1114 181 Barton, C. V. M. *et al*. Effects of elevated atmospheric [CO₂] on instantaneous transpiration efficiency at leaf and canopy scales in Eucalyptus saligna. *Glob. Change Biol.* **18**, 585-595 (2012).

1117 182 Savvides, A. M. & Fotopoulos, V. Two inexpensive and non-destructive techniques to correct for Smaller-Than-Gasket Leaf Area in gas exchange measurements. *Front. Plant Sci.* **9**, 548-548 (2018).
1120 Acknowledgements

1121 This study was supported by National Natural Science Foundation of China (41991230, 41988101),

1122 the Second Tibetan Plateau Scientific Expedition and Research Program (Grant

1123 No.2019QZKK0405) and the Xplorer Prize.

1124

1125 Author contributions

1126 S.P., formulated the review and identified the themes to be covered. X.L. performed the analyses

1127 and drafted the figures. X.L., S.P. and A.C. wrote the first draft of the manuscript. C.H., B.F.,

1128 L.Z.X.L., J.H., J.S., A.M.B., T.F.K., T.R.M., Y.W., X.W., T.W., Y.Y., M.L.R. reviewed and edited

1129 the manuscript before submission. All authors made substantial contributions to the discussion of

1130 content.

1131

1132 Competing interests

1133 The authors declare no competing interests.

1134

1135 Peer review information

1136 Nature Reviews Earth & Environment thanks Aristeidis Koutroulis, Sujong Jeong and the other,

1137 anonymous, reviewer(s) for their contribution to the peer review of this work.

1138

1139 Publisher's note

1140 Springer Nature remains neutral with regard to jurisdictional claims in published maps and

1141 institutional affiliations.
1142

1143 **Supplementary information**

1144 Supplementary information is available for this paper at https://doi.org/10.1038/s415XX-XXX-

1145 XXXX-X

1146
1147 **Key points**

1148 1. Atmospheric, agricultural, hydrological and ecological indices of aridity reveal strongly
1149 divergent trends since 1950 and into the near future.

1150 2. Warming-driven increases in vapour pressure deficit hastens evaporative water loss and
1151 depletes surface moisture, in turn, amplifying atmospheric drying through land-atmosphere
1152 feedbacks.

1153 3. Plant stomatal closure under elevated CO₂ reduces transpiration and compensates for the
1154 adverse effect of higher vapour pressure deficit for plant growth, explaining the co-occurrence
1155 of ecosystem greening and atmospheric drying in drylands.

1156 4. The physiologically-induced lowering of evapotranspiration under rising CO₂, along with the
1157 strong limitation by soil moisture, disconnects atmospheric drying and hydrological responses
1158 in drylands.

1159 5. With rapid climate change and population growth, anthropogenic water demand in drylands is
1160 projected to increase by ~270% by 2090s, exacerbating current water-resource scarcity.

1161 6. As future water deficits are driven mainly by increasing water demand, sustainable water-
1162 resource management and water-conservation technologies are needed to balance the socio-
1163 economic demands for water resources while maintaining healthy dryland ecosystems.

1164
1165
1166
1167
1168
1169
1170
<table><caption>Table 1. Trend statistics for different aridity metrics and databases.</caption><thead><tr><th rowspan="2">Metric and database</th><th colspan="3">Averaged change over the global dryland</th><th colspan="3">Areal change of the global dryland (% decade<sup>-1</sup>)</th></tr><tr><th>1948-2016</th><th>1979-2016</th><th>Near future</th><th>1948-2016</th><th>1979-2016</th><th>Near future</th></tr></thead><tbody><tr><td>Data-based VPD<br>(10<sup>-2</sup> kPa yr<sup>-1</sup>)</td><td>0.12 ± 0.01<br>(**)</td><td>0.16 ± 0.02<br>(**)</td><td>—</td><td>0.55 ± 0.11<br>(**)</td><td>1.49 ± 0.19<br>(**)</td><td>—</td></tr><tr><td>ESMs VPD<br>(10<sup>-2</sup> kPa yr<sup>-1</sup>)</td><td>0.15 ± 0.002<br>(1948-2005, **)</td><td>0.25 ± 0.04<br>(1979-2005, **)</td><td>0.21 ± 0.01<br>(2006-2100, **)</td><td>0.65 ± 0.07<br>(1948-2005, **)</td><td>1.15 ± 0.21<br>(1979-2005, **)</td><td>1.01 ± 0.03<br>(2006-2100, **)</td></tr><tr><td>Data-based Al<br>(10<sup>-3</sup> yr<sup>-1</sup>)</td><td>-0.02 ± 0.08<br>(n.s.)</td><td>-0.13 ± 0.12<br>(n.s.)</td><td>—</td><td>0.13 ± 0.06<br>(**)</td><td>0.05 ± 0.15<br>(n.s.)</td><td>—</td></tr><tr><td>Data-based Al_CO<sub>2</sub><br>(10<sup>-3</sup> yr<sup>-1</sup>)</td><td>0.04 ± 0.08<br>(n.s.)</td><td>0.20 ± 0.12<br>(n.s.)</td><td>—</td><td>0.08 ± 0.06<br>(n.s.)</td><td>-0.02 ± 0.15<br>(n.s.)</td><td>—</td></tr><tr><td>ESMs Al<br>(10<sup>-3</sup> yr<sup>-1</sup>)</td><td>-0.07 ± 0.04<br>(1948-2005, *)</td><td>0.16 ± 0.15<br>(1979-2005, n.s.)</td><td>-0.11 ± 0.02<br>(2006-2100, **)</td><td>0.14 ± 0.03<br>(1948-2005, **)</td><td>0.02 ± 0.10<br>(1979-2005, *)</td><td>0.20 ± 0.02<br>(2006-2100, **)</td></tr><tr><td>ESMs Al_CO<sub>2</sub><br>(10<sup>-3</sup> yr<sup>-1</sup>)</td><td>-0.03 ± 0.04<br>(1948-2005, n.s.)</td><td>0.20 ± 0.15<br>(1979-2005, n.s.)</td><td>-0.07 ± 0.02<br>(2006-2100, **)</td><td>0.11 ± 0.03<br>(1948-2005, **)</td><td>-0.03 ± 0.10<br>(1979-2005, n.s.)</td><td>0.15 ± 0.01<br>(2006-2100, **)</td></tr><tr><td>GLDAS soil moisture (% yr<sup>-1</sup>)</td><td>-0.015 ± 0.007<br>(1948-2010, **)</td><td>0.014 ± 0.016<br>(1979-2010, n.s.)</td><td>—</td><td>0.09 ± 0.08<br>(1948-2010, n.s.)</td><td>-0.12 ± 0.18<br>(1979-2010, n.s.)</td><td>—</td></tr><tr><td>TerraClimate soil moisture (% yr<sup>-1</sup>)</td><td>-0.061 ± 0.038<br>(1980-2016, n.s.)</td><td>0.032 ± 0.052<br>(1980-2016, n.s.)</td><td>—</td><td>0.05 ± 0.05<br>(1958-2015, n.s.)</td><td>-0.12 ± 0.10<br>(1979-2015, n.s.)</td><td>—</td></tr><tr><td>GLEAM soil moisture (% yr<sup>-1</sup>)</td><td>—</td><td>0.047 ± 0.022<br>(1980-2016, **)</td><td>—</td><td>—</td><td>-0.22 ± 0.16<br>(1980-2016, n.s.)</td><td>—</td></tr><tr><td>DGVMs soil moisture (% yr<sup>-1</sup>)</td><td>-0.017 ± 0.009<br>(**)</td><td>-0.045 ± 0.017<br>(n.s.)</td><td>—</td><td>0.09 ± 0.05<br>(*)</td><td>0.13 ± 0.10<br>(n.s.)</td><td>—</td></tr><tr><td>ESMs soil moisture (% yr<sup>-1</sup>)</td><td>-0.010 ± 0.002<br>(1948-2005, **)</td><td>-0.026 ± 0.006<br>(1979-2005, **)</td><td>-0.014 ± 0.001<br>(2006-2100, **)</td><td>0.05 ± 0.02<br>(1948-2005, **)</td><td>0.06 ± 0.05<br>(1979-2005, n.s.)</td><td>0.13 ± 0.01<br>(2006-2100, **)</td></tr><tr><td>River records (mm yr<sup>-2</sup>)</td><td>-0.192 ± 0.120<br>(1948-2016, n.s.)</td><td>0.203 ± 0.190<br>(1948-2016, n.s.)</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>LORA runoff (mm yr<sup>-2</sup>)</td><td>—</td><td>0.106 ± 0.055<br>(1980-2012, *)</td><td>—</td><td>—</td><td>-0.33 ± 0.16<br>(1980-2012, **)</td><td>—</td></tr><tr><td>DGVMs runoff (mm yr<sup>-2</sup>)</td><td>-0.075 ± 0.038<br>(**)</td><td>0.012 ± 0.076<br>(n.s.)</td><td>—</td><td>0.06 ± 0.07<br>(n.s.)</td><td>-0.21 ± 0.16<br>(n.s.)</td><td>—</td></tr><tr><td>ESMs runoff (mm yr<sup>-2</sup>)</td><td>-0.005 ± 0.010<br>(1948-2005, n.s.)</td><td>0.021 ± 0.036<br>(1979-2005, n.s.)</td><td>0.011 ± 0.005<br>(2006-2100, **)</td><td>0.03 ± 0.02<br>(1948-2005, *)</td><td>-0.17 ± 0.08<br>(1979-2005, **)</td><td>-0.02 ± 0.01<br>(2006-2100, *)</td></tr><tr><td>GIMMS NDVI<br>(10<sup>-2</sup> yr<sup>-1</sup>)</td><td>—</td><td>0.024 ± 0.006<br>(1982-2016, **)</td><td>—</td><td>—</td><td>-0.64 ± 0.14<br>(1982-2016, **)</td><td>—</td></tr><tr><td>MODIS LAI<br>(10<sup>-2</sup> yr<sup>-1</sup>)</td><td>—</td><td>0.33 ± 0.06<br>(2000-2016, **)</td><td>—</td><td>—</td><td>-1.55 ± 0.19<br>(2000-2016, **)</td><td>—</td></tr><tr><td>DGVMs GPP (gC m<sup>-2</sup> yr<sup>-2</sup>)</td><td>1.0 ± 0.1<br>(**)</td><td>1.4 ± 0.2<br>(**)</td><td>—</td><td>-0.72 ± 0.04<br>(**)</td><td>-0.95 ± 0.10<br>(**)</td><td>—</td></tr><tr><td>ESMs GPP (gC m<sup>-2</sup> yr<sup>-2</sup>)</td><td>1.5 ± 0.0<br>(1948-2005, **)</td><td>1.6 ± 0.1<br>(1979-2005, **)</td><td>1.4 ± 0.0<br>(2006-2100, **)</td><td>-0.75 ± 0.02<br>(1948-2005, **)</td><td>-0.95 ± 0.06<br>(1979-2005, **)</td><td>-0.67 ± 0.02<br>(2006-2100, **)</td></tr></tbody></table>

1172

Slope (± 1-S.D.) and statistical significance of the linear regression against time for each aridity metric and database. Unless otherwise stated in parentheses, the period over which trends are quantified are shown in the column heading. Statistical significance is determined using a t-test, with symbols "*", "**" and "n.s." denoting *p* < 0.05, *p* < 0.1 and *p* > 0.1, respectively. Trends for vapour pressure deficit (VPD) and aridity index (AI) are calculated using the mean of multiple datasets, and those for Dynamic Global Vegetation Models (DGVMs) and CMIP5 Earth System Models (ESMs) using the mean of multiple models. Note that the trend unit for averaged change is metric-dependent, but that for areal change it is the same across metrics (% decade<sup>-1</sup>).

1173

1174

1175

1176

1177

1178
**Figure 1** | Global drylands and ecohydrological conditions. The extent and classification of Aridity Index-defined drylands for 1961-1990, based on the TerraClimate dataset with high (~4 km) spatial resolution¹⁷². The cyan dots illustrate the density of vegetation cover for 1982-1990 (based on the GIMMS Normalized Difference Vegetation Index), with larger dots indicating denser vegetation cover. Note that all analyses in Figs. 2 and 3 focus solely on warm drylands (drylands south of 50°N), where all land surface elements routinely experience water stress.

**Figure 2** | Past and future dryland changes evaluated by five different aridity metrics. a-e | Various observational and model-derived anomalies of vapour pressure deficit (VPD) (panel a), aridity index (AI) or AI\_CO₂ that additionally accounts for CO₂ physiological impacts (panel b), soil moisture (panel c), runoff (panel d) and gross primary production (GPP), normalized difference vegetation index (NDVI) and leaf area index (LAI) (panel e), all averaged over AI-defined baseline regions of drylands for 1961-1990. f-j | as in a-e, but anomalies of the fraction of water-stressed land areas (drylands) evaluated by VPD (f_atm) (panel f), AI (f_AI or f_AI\_CO₂) (panel g), soil moisture (f_soil) (panel h), runoff (f_hyd) (panel i) and GPP, NDVI or LAI (f_veg) (panel j). Similar to f_AI (regions with AI < 0.65), f_atm, f_soil, f_hyd and f_veg are computed using threshold values of the corresponding metric (Supplementary Methods). Anomalies are computed by subtracting the climatological mean of 1961-1990 (or a subset of years during this period depending on the temporal coverage of data). The shaded areas represent the 95% confidence intervals of multiple data sources (for VPD and AI) or model results (for Dynamic Global Vegetation Models (DGVMs) or Earth System Models (ESMs)). The ESM results are derived from CMIP5 under the “historical” (1948-2005) and “RCP4.5” (2006-2100) scenarios. These different metrics present divergence in aridity changes and rates of global dryland expansion in both the past and the future. Trends of each metric and dataset are summarized in Table 1.

**Figure 3** | Continental assessment of future dryland changes. Projected future changes in the fraction of drylands and the four dryland subcategories (dry sub-humid, semi-arid, arid and hyper-arid; columns) as presented by f_atm (panels a-e), f_AI and f_AI\_CO₂ (panels f-j, left and right half of symbols, respectively), f_soil (panels k-o), f_hyd (panels p-t) and f_veg (panels u-y). Each circle represents the difference between dryland area (as a percentage of the sub-continent area) of a future period (1.5°C or 2°C warmer than the pre-industrial level, or the 2090s; all under the CMIP5 RCP4.5 scenario) and that of the 1961-1990 baseline (under the CMIP5 historical scenario).
Changes in $f_{atm}$, $f_{soil}$, $f_{hyd}$ and $f_{veg}$ are computed using threshold values of the corresponding metric (Supplementary Methods). Continental dryland regions labelled “I-VI” are marked with red boxes in panel z, which also illustrates the spatial distribution of regions where vegetation growth indicates a conversion from drylands to non-drylands in the near future (for 1.5 °C warmer, 2 °C warmer or the 2090s, relative to 1961-1990), corresponding to panels **u-y**. It is projected that in the future, the global dryland area will expand based on $f_{atm}$, $f_{AI}$ and $f_{soil}$, but contract based on $f_{hyd}$ and $f_{veg}$, with regionally dependent magnitude and/or sign of changes.

**Figure 4** | **Physical and physiological mechanisms for dryland aridity changes.** **a** | Schematic representation of processes underlying aridity changes of the atmospheric, hydrological, ecological and socio-economic systems, under warming and rising atmospheric CO₂. Circled symbols of ‘-’, ‘+’ and ‘?’ represent a negative, positive or potentially unknown sign of impact, respectively. **b** | Mechanisms for the shifting partitioning of precipitation between AET and runoff under warming and elevated CO₂. The curve shows the Budyko framework¹⁷³,¹⁷⁴ that links the partitioning of precipitation (P) into AET (green shaded area) and runoff (blue shaded area) to surface aridity level (defined by AI). The symbol ‘Δ’ denotes change of the corresponding quantity under warming and elevated CO₂. Under conditions of warming and decreasing AI values, the AET increase (vertical black arrow) cannot keep up with the PET increase (horizontal black arrow), owing to the water limitations and the physiological regulations of plant water loss under elevated CO₂.

**Figure 5** | **Dryland anthropogenic water stress under climatic and socio-economic changes.**
Historical and future changes of total anthropogenic water for North America (**panel a**), Northern Africa (**panel b**), Eurasia (**panel c**), South America (**panel d**), Southern Africa (**panel e**), Australia (**panel f**), and global drylands (**panel g**); for map of regions, see Fig. 3z. The water demand (D) is presented as a sum of agricultural, domestic and industrial water withdrawal, and the water supply (S) is mainly surface runoff. The y-axis scale is different for D and S. The time series are derived from the ensemble mean of three global hydrological models (GHMs, including H08, MATSIRO and LPJml) under the SSP2-RCP6.0 scenario. Arrows show the amount of D and S during 2090s, with numbers on the right showing the relative changes to the 1961-1990 baseline.
**Figure 6 | Conceptual diagram of future dryland aridity changes.** Present-day (panel a) versus future (panel b) aridity conditions of the atmospheric, hydrological, ecological and socio-economic systems over drylands. Future changes of dryland ecohydrological variables are based on CMIP5 simulations, without accounting for direct human interference. In panel b, red symbols of '+' and '-' in brackets represent an overall increase and decrease in the corresponding quantity respectively, relative to its present-day level. Note that the potential spatial heterogeneity of ecohydrological changes is not illustrated. The anticipated water demand changes (in dashed arrow) of the human society are based on global hydrological model simulations. VPD: vapour pressure deficit.
1270 Box 1. The multifaceted features and definitions of “aridity”.

1271 Aridity is a long-term state of water scarcity, which measures “the degree to which a climate lacks
1272 effective, life-promoting moisture”¹⁷⁵. In this sense, aridity is essentially different from drought
1273 which tracks short-term (days to years) departures from normal surface water conditions. Owing
1274 to the limited availability of surface soil moisture measurements, the Aridity Index (AI) which only
1275 requires meteorological measurements is popular in dryland studies. AI calculates the balance
1276 between the atmospheric water supply to the land (precipitation) and its demand from the land
1277 surface (potential evapotranspiration, PET). The PET formulation is often based on the Penman-
1278 Monteith equation¹⁷⁶ that requires meteorological inputs of net solar radiation (Rn), temperature
1279 (T), vapour pressure deficit (D), 2-m wind speed (u2), psychrometric constant (γ), and the slope
1280 of saturation vapour-pressure with temperature (Δ) (Eqn. 1).

$$
\text{PET} = \frac{0.408 \Delta R_n + \gamma \frac{900}{T + 273} u_2 D}{\Delta + \gamma \left(1 + (0.34 + 0.00024(c_a - 300)) u_2\right)} \quad (1)
$$

1281 Where 0.00024(cₐ-300) accounts for the effect of rising atmospheric CO₂ concentration (cₐ, ppm)
1282 on surface stomatal resistance (fixed as 0.34 in the original PET parameterisation), with the
1283 coefficients estimated from CMIP5 model outputs forced by rising cₐ under non-water-stressed
1284 conditions⁴⁵. Drylands are traditionally defined by the United Nations Environment Programme⁴²
1285 as areas with AI≤0.65, and can be further sub-categorised into dry sub-humid (0.65 > AI ≥ 0.5),
1286 semi-arid (0.5 > AI ≥ 0.2), arid (0.2 > AI ≥ 0.05) and hyper-arid (AI < 0.05) regions¹⁰,⁴².

1287
1288 Importantly, aridity itself is a highly complex concept on which there are numerous specific
1289 perspectives. Atmospheric aridity describes high atmospheric demand for water, and is measured
1290 by VPD or relative humidity⁴⁸,⁷²; soil moisture (or agricultural) aridity describes a state of soil
1291 moisture stress<sup>57,177</sup>; hydrological aridity describes a deficit of surface runoff<sup>178</sup>; ecological aridity
1292 describes a state of insufficient moisture to support vegetation growth, and is often related to
1293 reduction (or reduced capacity) of plant photosynthetic uptake of CO<sub>2</sub> (ref. <sup>36</sup>).

1294
1295 Although aridity means an excess of water demand over available supply for all Earth system
1296 processes, both the demand and supply sides differ substantially among them<sup>36,37,40,46,155</sup>. For
1297 instance, soil moisture is supplied by precipitation and glacier melt water, and the water demand is
1298 determined by plant transpiration and soil evaporation. Plants extract moisture from soils to live
1299 and grow, and the water demand is determined by atmospheric dryness and plant physiology<sup>37</sup>.

1300 Livestock and humans demand water to survive, which is provided mostly by rivers, lakes and
1301 groundwater reservoirs. Such different Earth system processes involved in the depiction of demand
1302 and supply for water cause these metrics to diverge in response to elevated CO<sub>2</sub>.

1303
Box 2. Characterising CO₂ physiological impacts on dryland productivity

Owing to the confounding effect of climate change, it is difficult to quantify dryland ecosystem responses to elevated CO₂ with empirical statistical models and historical records. Dynamic Global Vegetation Models (DGVMs) (Supplementary Table 2) participating in the “Trends in net land-atmosphere carbon exchange” (TRENDY; Supplementary Methods) project enable the quantitative characterisation of CO₂ physiological forcings, for example, using the simulation S1 forced by varying CO₂ but fixed climate¹⁷⁹. Higher CO₂ enhances gross primary production (GPP) through two parallel physiological processes: the direct CO₂ fertilization effect that enhances productivity while simultaneously consumes more water; and the indirect physiological effect which reduces stomatal conductance and thus conserves water for additional carbon uptake. The following analysis extracts contributions of the two counteracting mechanisms to dryland GPP changes.

GPP can be represented as the product of canopy-scale transpiration ($E_t$) and canopy-scale water-use efficiency (WUE$_c$):

$$GPP = E_t \cdot WUE_c \qquad (2)$$

By applying a differential transformation, fractional GPP changes are presented as:

$$\frac{dGPP}{GPP_0} = \frac{dE_t}{E_{t0}} + \frac{dWUE_c}{WUE_{c0}} \qquad (3)$$

where the subscript ‘0’ denotes the value for a baseline period (1948-1957). For a fixed precipitation amount, the strong water limitation for dryland ecosystems determines that $E_t$ is approximately conserved²⁸. For example, DGVMs (in the simulation forced by both varying CO₂
and climate) estimate a slight $E_t$ increase by +0.9% from 1948-1957 to 2007-2016. Hence, WUE$_c$ changes can almost fully explain the CO$_2$-driven increase of dryland GPP.

Warm and arid ecosystems often have simple aboveground structure, typically with leaf area index ($L$) less than unity. This means that an increase in $L$ would usually capture, linearly, more light and thus be proportionally related to enhanced transpiration and productivity$^{28,180}$. Hence, leaf-level CO$_2$ assimilation ($A$) and water loss ($E_{t,l}$) can be approximated as GPP and transpiration per unit leaf area (Eqn. 4). Under this assumption, WUE$_c$ can be directly scaled from the WUE of individual leaves (WUE$_l$) (Eqn. 5), as robustly supported by field experiments$^{181,182}$. Hence,

$$E_t = \int E_{t,l} \approx E_{t,l}L; \quad \text{GPP} = \int A \approx AL \quad (4)$$

$$WUE_c \approx WUE_l = \frac{A}{E_{t,l}} \quad (5)$$

By integrating Eqns. 3-5 to Eqn. 6, fractional changes of WUE$_c$ can be separated into contributions from changes of leaf-level carbon and water fluxes.

$$\frac{dWUE_c}{WUE_{c0}} \approx \frac{dWUE_l}{WUE_{l0}} = \frac{dA}{A} + \left( -\frac{dE_{t,l}}{E_{t,l0}} \right) \quad (6)$$

Eqn. 6 enables a disaggregation of the two physiological response. The first term ($dA/A_0$) can be interpreted as the leaf-scale CO$_2$ fertilization effect, and the second term ($-dE_{t,l}/E_{t,l0}$) as the leaf-scale CO$_2$ water savings effect. By integrating these equations and DGVM simulations, the water-saving effect is estimated to contribute 52% of the dryland GPP increase from 1948-1957 to 2007-2016, much higher than the effect of CO$_2$ fertilization (18%) and climate change (18%).
1341 Toc Blurb

1342 Estimates of global dryland changes are often conflicting. This Review discusses and quantifies observed

1343 and projected aridity changes, revealing divergent responses between atmospheric and ecohydrological

1344 metrics that can be explained by plant physiological responses to elevated CO₂.

1345

1346

1347

1348

1349
**Figure 1** | Global drylands and ecohydrological conditions. The global map shows the extent and classification of AI-defined drylands for 1961-1990, based on the TerraClimate dataset with high (~4 km) spatial resolution¹⁶⁹. The cyan dots show the density of vegetation cover for 1982-1990 (based on the GIMMS NDVI), with larger dots indicating denser vegetation cover. Note that all analyses in Figs. 2 and 3 focus solely on warm drylands (drylands south of 50°N), where all land surface elements routinely experience water stress.

<figure><img src="image_3.png" /></figure>
**Figure 2** | Past and future dryland changes evaluated by five different aridity metrics. a-e | Anomalies of VPD (a), AI (or AI_CO₂ that additionally accounts for CO₂ physiological impacts) (b), soil moisture (c), runoff (d) and GPP, NDVI or LAI (e), averaged over AI-defined baseline regions of drylands for 1961-1990. f-j | Anomalies of the fraction of water-stressed land areas (drylands) evaluated by VPD (f_atm) (f), AI (f_AI or f_AI_CO₂) (g), soil moisture (f_soil) (h), runoff (f_hyd) (i) and GPP, NDVI or LAI (f_veg) (j). Similar to f_AI (regions with AI < 0.65), f_atm, f_soil, f_hyd and f_veg are computed using threshold values of the corresponding metric (Supplementary Methods). The anomalies are computed by subtracting the climatological mean of 1961-1990 (or a subset of years during this period depending on the temporal coverage of the input data). The horizontal dashed lines are zero lines. The shaded areas represent the 95% confidence intervals of multiple data sources (for VPD and AI) or model results (for DGVMs or ESMs). The ESM results are derived from CMIP5 under the “historical” (1948-2005) and “RCP4.5” (2006-2100) scenarios. Trends of each metric and dataset are summarised in Table S5.

<figure><img src="image_4.png" /></figure>
**Figure 3** | **Continental assessment of future dryland changes.** Projected future changes in the fraction of drylands and the four dryland subcategories (dry sub-humid, semi-arid, arid and hyper-arid; columns) as presented by *f*<sub>atm</sub> (**a-e**), *f*<sub>Al</sub> (left half of symbols) or *f*<sub>Al_CO2</sub> (right half of symbols) (**f-j**), *f*<sub>soil</sub> (**k-o**), *f*<sub>hyd</sub> (**p-t**) and *f*<sub>veg</sub> (**u-y**). For each panel, shown is the difference between dryland area (as a percentage of the sub-continent area) of a future period (1.5 °C or 2 °C warmer than the pre-industrial level, or the 2090s; all under the CMIP5 RCP4.5 scenario) and that of the 1961-1990 baseline (under the CMIP5 historical scenario). Continental drylands labelled “I-VI” are marked with red boxes in the map, panel **z**. For each continent, changes in *f*<sub>atm</sub>, *f*<sub>soil</sub>, *f*<sub>hyd</sub> and *f*<sub>veg</sub> are computed using threshold values of the corresponding metric (Supplementary Methods). **z** | Spatial distribution of regions where vegetation growth indicates a conversion from drylands to non-drylands in the near future (for 1.5 °C warmer, 2 °C warmer or the 2090s, relative to 1961-1990), corresponding to panels **u-y**.
<figure><img src="image_5.png" /></figure>
**Figure 4** | **Physical and physiological mechanisms for dryland aridity changes. a** | Schematic representation of processes underlying aridity changes of the atmospheric, hydrological, ecological and socio-economic systems, under warming and rising atmospheric CO₂. Circled symbols of ‘-’, ‘+’ and ‘?’ represent a negative, positive or potentially unknown sign, respectively, on the quantity the corresponding arrow points to. **b** | Mechanisms for the shifting partitioning of precipitation between AET and runoff under warming and elevated CO₂. The curve shows the Budyko framework¹⁷⁰,¹⁷¹ that links the partitioning of precipitation (P) into AET (green shaded area) and runoff (blue shaded area) to surface aridity level (defined by AI). The symbol ‘Δ’ denotes change of the corresponding quantity under warming and elevated CO₂. Under conditions of warming and decreasing AI values, the AET increase (vertical black arrow) cannot keep up with the PET increase (horizontal black arrow), due to the water limitations and the physiological regulations of plant water loss under elevated CO₂.

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<figure><img src="image_8.png" /><figcaption>**Figure 5** | Dryland anthropogenic water stress under climatic and socio-economic changes. **a-f** | Historical and future changes of total anthropogenic water, for the six dryland sub-continents displayed in Fig. 3z, respectively. **g** shows the sum of the six dryland sub-continents. The water demand (D) is presented as a sum of agricultural, domestic and industrial water withdrawal, and the water supply (S) is mainly surface runoff. The y-axis scale is different for D and S. The time series are derived from the ensemble mean of three global hydrological models (GHMs, including H08, MATSIRO and LPJml) under the SSP2-RCP6.0 scenario. Arrows show the amount of D and S during 2090s, with numbers on the right showing the relative changes to the 1961-1990 baseline.</figcaption></figure>
**Figure 6** | Conceptual diagram of future dryland aridity changes. a Present-day versus b future aridity conditions of the atmospheric, hydrological, ecological and socio-economic systems over drylands. Future changes of dryland ecohydrological variables are based on CMIP5 ESM simulations, without accounting for direct human interference. In panel b, red symbols of '+' and '-' in brackets represent an overall increase and decrease in the corresponding quantity respectively, relative to its present-day level. Note that the potential spatial heterogeneity of ecohydrological changes is not illustrated in this diagram. The anticipated water demand changes (in dashed arrow) of the human society are based on global hydrological model (GHM) simulations.

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