Wang and Zhang Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-020-00325-3(2020) 7:11

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open AccessArid/humid patterns over Asia in response
to national-committed emission reductions
under the Paris agreement
Fang Wang1*†

and Jintao Zhang1,2†

Abstract
The evolution of regional arid/humid patterns caused by global climate change deserves attention. We investigatedthe changes in the area of arid/humid climate regions over Asia under the aegis of national-committed emission
reductions to comply with the Paris Agreement using an ensemble of 17 global climate models (GCMs) from theFifth Coupled Climate Model Intercomparison Project (CMIP5) archives. Our results indicated an overall trend
towards aridity in a future warmer climate based on an aridity index (AI), defined as the ratio of precipitation topotential evapotranspiration (PET). In the future, the humid region will contract and the arid region and arid/humidtransition zones will expand. Compared with the present period, arid/humid region switch is projected to occur ina land area of 2512.8 × 103 km2 over Asia under the Intended Nationally Determined Contributions (INDC)-pledge
scenario. If global warming is controlled to the ambitious 2.0 °C and 1.5 °C scenarios instead of that of the INDCpledge scenario, a decrease in the changes in the area of arid/humid regions by more than half would be
expected. In addition, the arid/humid regions with continuous global warming are estimated to contract/expand
greatly. Changes in the spatial pattern of the arid/humid zones over Asia show strong similarities between multimodel simulations, although there are some differences in detail.
Keywords: Arid/humid patterns, Aridity index, Climate change, Asia, INDC

Introduction
According to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), the global mean temperature increased 0.85 °C (0.65–1.06 °C)
between 1880 and 2012, and a larger increase is projected in the future (IPCC 2014). Changes in regional
aridity are among the primary effects anticipated underglobal warming (Dai 2012, Trenberth et al. 2013, Greveand Seneviratne 2015). Changes in moisture conditions
greatly affect the distribution of arid/humid patterns(Yin et al. 2015), but accurately quantifying those effects
remains a challenge. Projected changes in temperatureand precipitation will cause shifts in arid/humid climate
zones worldwide (Hanf et al. 2012, Feng et al. 2014,* Correspondence: wangf@igsnrr.ac.cn†
Fang Wang and Jintao Zhang are the co-first authors, and they contributed
equally to this work.
1
Key Laboratory of Land Surface Pattern and Simulation, Institute ofGeographic Sciences and Natural Resources Research, Chinese Academy of
Sciences, Beijing 100101, China
Full list of author information is available at the end of the article

Rohli et al. 2015). Present-day climate types will gradually shift towards warmer and drier types over thetwenty-first century according to the Representative
Concentration Pathway 8.5 (RCP8.5) scenario (Fenget al. 2014). The projected expansion of arid regions,semi-arid regions, or both has been investigated in West
Africa (Sylla et al. 2016), the Mediterranean (Gao andGiorgi 2008, Alessandri et al. 2014), and China (Li and
Ma 2013, Ma et al. 2019). However, to what extent future climate change will affect arid/humid regions in
Asia is unknown and differs among regions.
The Paris Agreement sets a limit of 2 °C on the global
mean temperature change and pursues efforts to limit
warming to 1.5 °C relative to preindustrial levels(UNFCCC 2015a, b). To achieve the goal of the Paris
Agreement, countries submitted national mitigationplans in the form of Intended Nationally Determined
Contributions (INDC) (UNFCCC 2019). Most past studies projected changes in the climate at a specific time in
the future on the basis of a series of greenhouse gas and© The Author(s). 2020 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.

Wang and Zhang Progress in Earth and Planetary Science(2020) 7:11

aerosol forcing scenarios, such as RCPs (van Vuuren
et al. 2011). In contrast, the focus of the Paris Agreement
and current mitigation commitments negotiated by governments is on global mean warming limits. Therefore, it
is more important to quantify projected climate changes
using the temperature targets associated with the current
mitigation commitments negotiated by governments rather than prearranged emission scenarios.
In our study, we examined the trend in hydroclimatic
variables using the INDC emission reduction scenarios forthe next few decades and investigated the changes in arid/
humid regions using observational climatology and an ensemble of comprehensive global climate models (GCMs)
from the Fifth Coupled Climate Model Intercomparison
Project (CMIP5). Specifically, potential evapotranspiration
was estimated using the revised Penman-Monteith modeland was then combined with precipitation to construct anaridity index to classify arid/humid climate types. Our results revealed the sensitivity of arid/humid patterns toprojected climate change and indicated the hotspot regions, providing a scientific foundation for making policy
with respect to climate change.

Data and methods
Emission scenarios

In this study, the INDC mitigation target scenarios basedon the Paris Agreement were analysed. We calculated
future quantified emission targets for each country using
the normalisation method and INDC emissions datasubmitted by 192 countries (UNFCCC 2019). Because
the published INDCs provide emission targets proposed
to be achieved by 2030, we extended the INDC scenarios
to the end of this century by analysing the IPCC AR5
scenario database. Detailed information is provided in
Text S1. We focused on the INDC-sustained mitigation
scenario (hereafter, INDC pledge; Fig. S1), which is consistent with the ‘continued action’ pathway reported inRogelj et al. (2016), Wang et al. (2018), and CAT (2017).
We further estimated the global mean warming (at theend of this century) response to the INDC-sustained
mitigation and determined that ΔTINDC ≈ 2.9 – 3.3 ° C(median = 3.1 °C) is the most likely temperature increase
range for the ‘continued action’ pathways of the INDCs(Zhang and Wang 2019) (Text S2).
In addition, we analysed the 2.0 °C and 1.5 °C
temperature target scenarios consistent with the Paris
Agreement. The 2.0 °C and 1.5 °C target scenarios are
based on the AR5 (IPCC 2014) and 1.5 °C Special Reportof the IPCC (IPCC 2018).
Data description

To derive the regional climate changes and the corresponding spatial shifts in the arid/humid zones acrossAsia, we adopted 17 state-of-the-art CMIP5 GCMs

Page 2 of 10(Taylor, Stouffer, and Meehl 2011). Basic information
about these GCMs is summarized in Table S1. The predictions by the mean of the ensemble of these GCMs are
better than that of any individual ensemble member because the uncertainty of the predictions corresponds tothe intermodel variability, which can be filtered (Reichler
and Kim 2008, Pierce et al. 2009). Therefore, the multimodel ensemble mean was used to reflect the future climatic characteristics. All model data were interpolated
to a common 0.5 × 0.5 horizontal grid for ensemble analyses, and, to treat all models equally, we selected thefirst run for the multiple-ensemble models.
The CRU (Climate Research Unit) Time Series dataset
v. 4.03, with a horizontal resolution of 0.5 × 0.5 based on
stationary observation (Harris et al. 2014), was used tovalidate the GCMs. Before simulation data are used to
study the impact of climate change, a linear correction
using the bias correction method is recommended to reduce the bias of climate model simulations comparedwith observed climate data (Durman et al. 2001) (TextS3). Here, the simulated climatic variables were adjusted
to have the same climatological annual mean as the real
observations over Asia for the baseline period 1961–
1990. While the general features of the observed arid/
humid patterns were reasonably reproduced by the corrected model data, mild biases were evident (Figs. S3and S4).
With respect to the social impact, the projected population distributions under different shared socioeconomic pathways (SSPs) (Jones and O’Neill 2016) wereused to investigate the population distribution in changing arid/humid climate regions. In this paper, we
present the population-related results estimated using
the population projected for 2100 under the SSP2 scenario because the results based on projections underother SSP scenarios are qualitatively similar.
Since the internal variability can lead to uncertainty ofclimate change projection (Hu et al., 2019), on the basisof previous research (Robert et al. 2014, Schleussner
et al. 2016, Huang et al. 2017), we used a time slice approach in which the spatial state at a specific warming
point with respect to ΔTINDC (or 2.0 °C, 1.5 °C) is takenfrom decadal time slices, with the respective mean
warming for each model taken separately. More generally, this time slice approach can relate various climate
indicators to global mean warming (see Text S4).
Arid/humid zone classification

The interplay between water supply and demand, including both precipitation (P) and potential evapotranspiration (PET), is critical to the projection of changes indryness (Cook et al. 2014, Greve and Seneviratne 2015,
McEvoy et al. 2016) and dryland dynamics (Huang et al.
2017). The aridity index (AI), expressed as the ratio of P

Wang and Zhang Progress in Earth and Planetary Science(2020) 7:11

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Fig. 1 Annual precipitation and PET across Asia for the present period. Annual precipitation (left) and PET (right) across Asia for the present
period (1985–2005) based on CRU observational data

to PET (Budyko 1974), is widely used as an indicator ofregional moisture conditions and is an effective index
for classifying arid/humid zones. In general, PET indicates the maximum water demand for an environment
to maintain its water balance, and P reflects the watersupply over large scales. Because PET observed overlarge regions is difficult to determine, we considered thePenman–Monteith model (Text S5), a widely usedmethod to simulate PET (Allen and Ingram 2002). It is
used to project long-term drought and dryland evolution
under climate change (Sherwood and Fu 2014, Huang
et al. 2017). Present-day P, PET, and AI based on theabove methods are displayed in Figs. 1 and 2.
Currently, more researchers have generally divided theclimate classification of drylands using the AI criteria
and considered that the PET estimated by the common

Penman–Monteith equation is closer to the real-world
conditions under all climates (e.g. Fu and Feng, 2014;
Yang et al., 2017). Based on the United Nations Convention to Combat Desertification classification, drylands
are characterised by P/PET < 0.65 and are further divided
into hyperarid (P/PET < 0.05), arid (0.05 < P/PET < 0.2),
semiarid (0.2 < P/PET < 0.5), and dry sub-humid (0.5 < P/
PET < 0.65) regions (Middleton and Thomas, 1997). In
this paper, we broadly classify the land surface into fourzones based on AI (AI=P/PET): humid, sub-humid, semiarid, and arid, with details given in Table 1.

Results
Changes in P and PET

Changes in multi-model-projected P and PET under
1.5 °C, 2.0 °C, and INDC-pledge scenarios relative to theFig. 2 Arid/humid climate regions across Asia for the present period. Arid/humid climate regions across Asia for the present period (1985–2005)
based on CRU observation data

Wang and Zhang Progress in Earth and Planetary Science(2020) 7:11

Table 1 Criteria for demarcating the arid/humid climate regionsof Asia according to AI
Arid/humid climate region

Aridity index (AI = P/PET)Humid

AI ≥ 0.65

Sub-humid

0.5 ≤ AI < 0.65

Semi-arid

0.2 ≤ AI < 0.5

Arid

AI < 0.2

present day are shown in Fig. 3. Annual precipitation increases moderately (< 10%) over most of Asia under the
INDC-pledge scenario, with the most prominent increase (up to 30% or more) in the northern periphery of
the Tibetan Plateau. By contrast, a decrease is seen insome regions in the Middle East, Central Asia, andsouthern China. There are less significant changes in theannual P under the 1.5 °C and 2.0 °C scenarios, although
the spatial distribution of variation is similar. The projected increasing precipitation across Asia can be mostly
explained by the thermodynamical mechanism, whichassumes fixed atmospheric flow patterns but increased
water vapour in the lower troposphere (Held and Soden,2006). However, the changes in atmospheric circulation

Page 4 of 10partially oppose this thermodynamic response in someregions (Allan, 2012).
On the other hand, annual PET (simulated using thePenman–Monteith model) is likely to significantly increase across almost the entire continent under the
INDC-pledge scenario, with a relatively uniform increaseof 0–20% in most lands. In general, the larger relative increases occur at the high latitudes. The changes in PET
under the 1.5 °C and 2.0 °C scenarios also show a spatial
pattern similar to but smaller than the results under the
INDC-pledge scenario. The overall change of the annual
PET is more significant than that in the annual P basedon the results of statistical tests (stippling in Fig. 3). The
significant increase in PET can be mainly explained by athermodynamical mechanism that the warming is causing a higher vapour pressure deficit and evaporative demand. PET is also affected by other factors such as net
radiation, wind, and humidity (Huang et al., 2017, 2019).
Future changes in the arid/humid pattern are determined by the combination of changes in P and PET.
The increase in PET is more remarkable than that in P
(columns 1 and 2 in Fig. 3), resulting the degree of aridity increases over most regions in Asia in the future

Fig. 3 Changes in annual precipitation, PET, and AI across Asia. Changes in annual precipitation (left column), PET (middle column), and AI (rightcolumn) across Asia (multi-model mean). The scenario used is indicated in the upper-left corner of each panel. Black stippling indicates areas
where at least 2/3 models agree on the sign of the changes for each grid

Wang and Zhang Progress in Earth and Planetary Science(2020) 7:11

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Fig. 4 Asian mean annual precipitation, PET, and AI as a function of warming level. The solid line is the multi-model mean, and the shaded areais the interquartile range(column 3 in Fig. 3). The average AI in Asia has a decreasing trend with continuous global warming and an
overall trend towards aridity (Fig. 4). The areas with remarkable decreases in the AI include southern China,
Japan, and northwest Russia, while the AI over the Tibetan Plateau increases significantly. The changes in thevalue of the AI do not fully represent the degree ofchange in arid/humid conditions (e.g. the AI in arid areas
is already very small, so the change in the AI is also
small compared with that of humid areas).
Changes in arid/humid regions over Asia

We estimated the changes in the area of arid/humid climate zones under different scenarios using the AI definedin the ‘Arid/humid zone classification’ section, first classify the arid/humid climate zones based on AI simulated
by separate models, and then analysed the multi-model
ensemble mean area of arid/humid zones. We found thatthe humid regions decreased in size while the arid regions
increased in size in future warmer climate. Comparedwith the present period, arid/humid region switch is projected to occur in a land area of 1427.4 (1279.2~1847.4,
range of 25–75% confidence interval for the multi-model
ensemble, and the same confidence interval applies tosubsequent values) × 103, 1674.6 (1490.2~1956.5) × 103,
and 2512.8 (2393.0~2646.2) × 103 km2 over Asia underthe 1.5 °C, 2.0 °C, and INDC-pledge scenarios, respectively, based on multi-model ensemble mean. The sizes of
the humid regions show an average net decrease of 113.0(− 39.7~171.3) × 103, 170.3 (− 10.7~356.1) × 103, and365.5 (160.7~924.9) × 103 km2 under the three above scenarios, respectively (where at least 71% of models agree
on the sign of change). Similarly, compared with thepresent period, the sizes of the arid and semi-arid regions
show an average net increase of 184.4 (86.4~358.1) × 103
and 45.0 (− 31.7~290.3) × 103 km2 under the INDCpledge scenario, respectively, with greater global warming(where 82% of models (for arid) and 71% of models (forsemi-arid) agree on the sign of change). However, thesub-humid region shows no significant change under thedifferent scenarios. The relative net changes in the areasof the humid, sub-humid, semi-arid, and arid regions
under the INDC-pledge scenario compare with present
period are − 1.52% (− 3.87~ − 0.68%), − 0.34% (− 9.02~
9.12%), 2.45% (− 0.44~ 4.19%), and 1.77% (0.83~ 3.45%),
respectively, with the most remarkable areal change occurring in the semi-arid regions.
If warming is at a lower level, Asia will benefit fromrobust reductions in changes in arid/humid regions(Fig. 5). The impact of changes avoided in arid/humid
regions by a lower level of global warming (e.g. 2.0 °C or
1.5 °C) compared with the INDC scenario was investigated using the following equation:
Avoid impact ¼

C INDC −C k
C INDC

where C represents the change in the areas of arid/
humid regions under a specific scenario compared withthe areas under present climatology and the subscript k

Fig. 5 Changes in the area of arid/humid regions over Asia that are
avoided in less warming scenarios. Left bars are the 2.0 °C scenario
compared with the INDC-pledge scenario, and right bars are the
1.5 °C scenario compared with the INDC-pledge scenario. Horizontal
lines denote multimodal median, and the length of the vertical lines
denotes the interquartile range

Wang and Zhang Progress in Earth and Planetary Science(2020) 7:11

indicates the different scenarios (i.e. 2.0 °C and 1.5 °C).
Our results showed that the lower warming of the 2.0 °C
target scenario instead of the INDC-pledge scenario will
help avoid an approximately 36%, 68%, and 55% increasein arid, semi-arid, and sub-humid regions, respectively,
and about a 71% decrease in humid regions. If global

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warming is further controlled to 1.5 °C, such remarkable
benefit of smaller changes in the areas of arid/humid regions would also be seen (Fig. 5).
We further examined the trend in the variation inarid/humid climate zones at different global warming
levels (Fig. 6). For the multi-model median, the area ofFig. 6 Areal and population proportion of arid/humid regions as a function of warming level over Asia. Areal proportion (a) and population
proportion (b) of humid, sub-humid, semi-arid, and arid regions as a function of warming level over Asia. The dashed lines are the linear trends,
while the regression equation and the Pearson’s correlation coefficient are labelled on the bottom-left of each subplots. The solid lines andshaded areas indicate multi-model medians and interquartile ranges, respectively. The population proportions at the different warming levels
were estimated from the 2100 population projection under the SSP2 scenario

Wang and Zhang Progress in Earth and Planetary Science(2020) 7:11

humid regions was reduced with continuous global
warming at a rate of 238.5 × 103 km2/°C, while the areasof sub-humid, semi-arid, and arid regions increased with
global warming at a rate of 58.8 × 103, 58.0 × 103, and121.7 × 103 km2/°C per year, respectively. The increase
and decrease trends with warming are meaningful only
when the associated projection uncertainties remain
within reasonable bounds. Inconsistent trends of area ofarid/humid regions at higher warming levels are evident
mainly attribute to the increasing uncertainties of GCMs
itself, and the inconsistent ensemble members (Fig. S2).
Compared to the humid and arid regions, the subhumid and semi-arid regions seem to have more significant intermodel variations—with larger variations athigh warming levels, which highlight the uncertainties inprojected arid/humid pattern between models.
Because regional aridity changes directly affect theamount of water on which human beings depend, weanalysed the population distribution in different future
arid/humid climate zones. To facilitate comparisons at
different global warming levels, the projected population
distribution in 2100 under the SSP2 scenario wasadopted to estimate the population proportion for the

Page 7 of 10

four arid/humid zones at different global warming levels(Fig. 6b). The population tended to be concentrated in
the humid (55.6%) and sub-humid (13.5%) areas, while asmaller population proportion was in the arid (18.9%)
and semi-arid (11.9%) areas (the number in brackets isthe present-day level). As global warming intensified, thepopulation proportion in humid regions decreased significantly, dropping to 54.0% under the INDC pledge,
based on the multi-model median. In contrast, the proportion of the population living in arid and semi-arid
areas increased with global warming under the INDC
pledge to 12.6% and 19.8%, respectively, whereas the
proportion of the population in the sub-humid region
showed no significant change under different warming
levels.
We took a closer look at the spatial distribution ofarid/humid regions over Asia in the warmer future, because the changes (compared with the present period)
under the 2.0 °C and 1.5 °C scenarios were not as obvious
as those under the INDC-pledge scenario; the results for
only the INDC-pledge scenario and based on multimodel mean AI are displayed in Fig. 7. Each type of climatic zone has a mild change, mainly on the boundary.

Fig. 7 Spatial distribution of humid (a), sub-humid (b), semi-arid (c), and arid (d) regions over Asia for the INDC-pledge scenario. The different
colours indicate changed and unchanged areas relative to the present period. The classification of arid/humid regions are based on multi-model
mean AIWang and Zhang Progress in Earth and Planetary Science(2020) 7:11

Specifically, the arid regions in West Asia expand to thenorth and east, while the southern margin of the arid regions in Northwest China contract to the north. The expansion and contraction of semi-arid and sub-humid
areas are obvious, but the net effect is an increase inarea (as most models show). The expansion of the semiarid zone is most prominent in northern China and theborder between Kazakhstan and Russia. There are large
differences between the changes in the sub-humid regions among the models, and the humid regions aretaken over by the sub-humid regions correspondingly.
The spatial pattern of arid/humid zones over Asia basedon simulated AI by separate models shows considerable
similarities, although there are some differences in thedetails (Figs. S5, S6, S7, S8).

Discussion
This study used an aridity index to classify arid/humid
climate regions over Asia and investigated changes inarid/humid patterns under future warming scenarios.
Our results indicated a significant contraction in thehumid region and an expansion in the arid region andthe arid/humid transition zone under the scenario ofnational-committed emission reductions under the Paris
Agreement. We quantified the change in the regional
arid/humid pattern based on the self-determined emission reduction commitments, which are the current national
mitigationcommitments
negotiatedbygovernments. These results are more practical thanthose in previous studies based on RCP or 2.0 °C/1.5 °C
scenarios. Our results showed an arid trend in Asia in a
warmer future. Upon examining the arid/humid region
switch, previous studies pointed out that the boundariesof the East Asian climate transition zone would expand
in both directions (Wang et al., 2017). The subtropicalhumid region in Southeast China showed a contraction,and the semi-arid and sub-humid regions in northern
China showed a southeast expansion by the end of thiscentury (Chan and Wu 2015; Li and Ma 2013; Huang
et al. 2017). The trends they gave are similar to thetrends in this study, but the values are slightly different.
The possible reasons for the differences include (1) different regions of analysis (the East Asia for Wang et al.
(2017), the China for Li and Ma (2013), the Asia in thisstudy, and the larger regions for Chan and Wu (2015)
and Huang et al. (2017)), and (2) different emission scenarios (the 2 °C warming scenario for Huang et al.
(2017), the RCP 4.5 and 8.5 for Wang et al. (2017), thehistorical scenario for Chan and Wu (2015), and thenational-committed emission scenarios in this study).
The differences in emission scenarios are potentiallymore important. In this study, several new emission scenarios were updated based on 192 countries’ INDC data,
and then the characteristics of the arid/humid patterns

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under the new emission scenarios were re-estimated.
We also evaluated how the areas of arid/humid climate
regions change with continuous global warming (fromcurrent level to > 4 °C). Our results provide useful information for policy design for climate change mitigation
and adaptation.
Changes in arid/humid patterns are caused by multiple
factors. Some scholars regard decreasing precipitation as
the main reason for increased drought in many tropicaland subtropical regions, along with changing atmospheric circulation and sea surface temperature (Dai2012, Fu et al. 2015). In addition, temperature change
may play an important role in shifting climatic regimes
with continuous global warming (Feng et al. 2014),
which leads to increasing deficits in water vapour pressure, increasing evaporation, and decreasing soil moisture. Our results show the amount of change in PET (atemperature-affected variable) is generally higher thanthat for precipitation in most regions over Asia, whichindicates that the increase in atmospheric water demandwith climate warming exceeds the increase in precipitation, and therefore affecting the arid/humid pattern. Thisfinding is essentially consistent with those of studies
based on the Palmer Drought Severity Index (PDSI)(Wang and Chen 2014). In addition, the arid/humid variations are generally affected by other factors, such as
southeast and southwest monsoons, plateau monsoon in
the Tibetan Plateau, westerly circulation, and West Pacific subtropical high (Liu et al. 2018; Yang et al. 2003).
Therefore, the changes in arid/humid patterns are theresults of complex effects of various factors.

Conclusions
Based on a combination of climate observational dataand simulated data based on CMIP5 GCMs, we simulated PET by applying the revised Penman-Monteithmodel to calculate the AI over Asia. Projected arid/
humid patterns corresponding to different levels of climate warming were then investigated. We founded thatthe amount of change in PET is generally higher thanthat for precipitation, which determine decreasing AI inmost regions. As the result, the humid region will contract significantly, whereas the semi-arid, and arid regions will expand. If global warming is limited to 2.0 °C
or 1.5 °C instead of the INDC-pledge scenario, a remarkable reduction of more than half in the switch between
the arid/humid regions is expected. Moreover, thehumid/arid areas are likely to contract/expand greatly
with continuous global warming. Mild changes in theboundary between different types of arid/humid climatic
zones in the future can be distinguished from those in
the present period. The simulated spatial patterns ofarid/humid zones over Asia from multiple models havestrong similarities but show some differences in detail.

Wang and Zhang Progress in Earth and Planetary Science(2020) 7:11

Supplementary information
Supplementary information accompanies this paper at https://doi.org/10.
1186/s40645-020-00325-3.
Additional file 1. Placed at the bottom of the last page due to too much text.
Abbreviations
AI: Aridity index; AR5: The Fifth Assessment Report; CMIP5: The Fifth Coupled
Climate Model Intercomparison Project; CRU: Climate Research Unit; AR5: The
Fifth Assessment Report; GCM: Global climate model; INDC: Intended
Nationally Determined Contributions; IPCC: Intergovernmental Panel on
Climate Change; P: Precipitation; PET: Potential evapotranspiration;
RCP: Representative Concentration Pathway; UNFCCC: United Nations
Framework Convention on Climate Change
Acknowledgements
We would like to thank three anonymous reviewers for their helpfulcomments. This work was supported by the National Key Research andDevelopment Program of China (2019YFC0507805, 2016YFA0602704), theStrategic Leading Science and Technology Program of the Chinese Academyof Sciences (XDA20020202), and the National Natural Science Foundation ofChina (41771050).
Authors’ contributions
writing—original draft preparation, J. Zhang; writing—extensively revised
writing, F. Wang. All authors read and approved the final manuscript.
Funding
This work was supported by the National Key Research and Development
Program of China (2019YFC0507805, 2016YFA0602704), the Strategic Leading
Science and Technology Program of the Chinese Academy of Sciences(XDA20020202), and the National Natural Science Foundation of China(41771050).
Availability of data and materials
The CMIP5 dataset is available in https://esgf-node.llnl.gov/projects/esgf-llnl/.
The CRU dataset is available in http://www.cru.uea.ac.uk/data.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Key Laboratory of Land Surface Pattern and Simulation, Institute ofGeographic Sciences and Natural Resources Research, Chinese Academy of
Sciences, Beijing 100101, China. 2College of Resources and Environment,
University of Chinese Academy of Sciences, Beijing 100049, China.
Received: 25 September 2019 Accepted: 14 February 2020

References
Alessandri A, De Felice M, Zeng N, Mariotti A, Pan Y, Cherchi A, Lee J-Y, Wang B,
Ha K-J, Ruti P, Artale V (2014) Robust assessment of the expansion andretreat of Mediterranean climate in the 21st century. Sci Rep 4:7211. https://doi.org/10.1038/srep07211
Allan RP (2012) Regime dependent changes in global precipitation. Climate
Dynamics 39(3):827–840. https://doi.org/10.1007/s00382-011-1134-x
Allen MR, Ingram WJ (2002) Constraints on future changes in climate and thehydrologic cycle. Nature 419:224. https://doi.org/10.1038/nature01092
Budyko (1974) Climate and life. Academic Press, New YorkCAT. (2017). "Addressing global warming." https://climateactiontracker.org/global/
temperatures/. addressed on November 6, 2019
Chan D, Qigang W (2015) Significant anthropogenic-induced changes of climateclasses since 1950. Sci Rep 5:13487. https://doi.org/10.1038/srep13487
Cook BI, Smerdon JE, Seager R, Coats S (2014) Global warming and 21st century
drying. Clim Dyn 43(9):2607–2627. https://doi.org/10.1007/s00382-014-2075-y
Dai A (2012) Increasing drought under global warming in observations andmodels. Nat Clim Chang 3:52. https://doi.org/10.1038/nclimate1633
Durman CF, Gregory JM, Hassell DC, Jones RG, Murphy JM (2001) A comparisonof extreme European daily precipitation simulated by a global and a regional

Page 9 of 10

climate model for present and future climates. Q J R Meteorol Soc 127(573):
1005–1015. https://doi.org/10.1002/qj.49712757316
Feng S, Hu Q, Huang W, Ho C-H, Li R, Tang Z (2014) Projected climate regime
shift under future global warming from multi-model, multi-scenario CMIP5
simulations. Global Planetary Change 112:41–52. https://doi.org/10.1016/j.
gloplacha.2013.11.002
Fu Q, Feng S (2014) Responses of terrestrial aridity to global warming. J Geophys
Res Atmospheres 119(13):7863–7875. https://doi.org/10.1002/2014JD021608
Fu YH, Zhao H, Piao S, Peaucelle M, Peng S, Zhou G, Ciais P, Huang M, Menzel A,
Peñuelas J, Yang S, Vitasse Y, Zeng Z, Janssens IA (2015) Declining global
warming effects on the phenology of spring leaf unfolding. Nature 526:104.
https://doi.org/10.1038/nature15402
Gao X, Giorgi F (2008) Increased aridity in the Mediterranean region undergreenhouse gas forcing estimated from high resolution simulations with aregional climate model. Global Planetary Change 62(3):195–209. https://doi.
org/10.1016/j.gloplacha.2008.02.002
Greve P, Seneviratne SI (2015) Assessment of future changes in water availability
and aridity. Geophys Res Lett 42(13):5493–5499. https://doi.org/10.1002/
2015GL064127
Hanf F, Zittel J, Spangehl T, Cubasch U (2012) Shifts of climate zones in multimodel climate change experiments using the Köppen climate classification.
Meteorol Z 21:111–123. https://doi.org/10.1127/0941-2948/2012/0344
Harris I, Jones PD, Osborn TJ, Lister DH (2014) Updated high-resolution grids ofmonthly climatic observations – the CRU TS3.10 dataset. Int J Climatol 34(3):623–642. https://doi.org/10.1002/joc.3711
Held IM, Soden BJ (2006) Robust responses of the hydrological cycle to global
warming. J Clim 19(21):5686–5699. https://doi.org/10.1175/JCLI3990.1
Hu K, Huang G, Xie SP (2019) Assessing the internal variability in multi-decadaltrends of summer surface air temperature over East Asia with a large
ensemble of GCM simulations. Climate Dynamics 52:6229. https://doi.org/10.
1007/s00382-018-4503-x
Huang J, Ma J, Guan X, Li Y, He Y (2019) Progress in semi-arid climate change
studies in China. Adv Atmos Sci 36(9):922–937. https://doi.org/10.1007/
s00376-018-8200-9
Huang J, Yu H, Dai A, Wei Y, Kang L (2017) Drylands face potential threat under
2 °C global warming target. Nat Clim Chang 7:417. https://doi.org/10.1038/
nclimate3275
IPCC (2014) Climate change 2014: synthesis report. Contribution of working
groups I, II and III to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change. Cambridge University Press, Cambridge, UK, andNew York, NY, USA
IPCC (2018) Global warming of 1.5 °C. Cambridge University Press, Cambridge,
UK, and New York, NY, USA
Jones B, O’Neill BC (2016) Spatially explicit global population scenarios consistent
with the shared socioeconomic pathways. Environ Res Lett 11(8):084003
Li MX, Ma ZG (2013) Soil moisture-based study of the variability of dry-wet
climate and climate zones in China. Chin Sci Bull 58(4):531–544. https://doi.
org/10.1007/s11434-012-5428-0
Liu J, Xu H, Deng J (2018) Projections of East Asian summer monsoon change at
global warming of 1.5 and 2°C. Earth Syst Dynamics 9(2):427–439. https://doi.
org/10.5194/esd-9-427-2018
Ma D, Deng H, Yin Y, Wu S, Zheng D (2019) Sensitivity of arid/humid patterns in
China to future climate change under a high-emissions scenario. J Geogr Sci
29(1):29–48. https://doi.org/10.1007/s11442-019-1582-5
McEvoy DJ, Huntington JL, Mejia JF, Hobbins MT (2016) Improved seasonaldrought forecasts using reference evapotranspiration anomalies. Geophys
Res Lett 43(1):377–385. https://doi.org/10.1002/2015GL067009
Middleton N, Thomas D (1997) World atlas of desertification (2nd Edn). UNEP,
London, 182p
Pierce DW, Barnett TP, Santer BD, Gleckler PJ (2009) Selecting global climate
models for regional climate change studies. Proc Natl Acad Sci 106(21):8441.
https://doi.org/10.1073/pnas.0900094106
Reichler T, Kim J (2008) How well do coupled models simulate today's climate?
Bull Am Meteorol Soc 89(3):303–312. https://doi.org/10.1175/BAMS-89-3-303
Robert V, Andreas G, Stefan S, Erik K, Annemiek S, Paul W, Thomas M, Oskar L,
Grigory N, Claas T, Daniela J (2014) The European climate under a 2 °C global
warming. Environ Res Lett 9(3):034006
Rogelj J, den Elzen M, Höhne N, Fransen T, Fekete H, Winkler H, Schaeffer R, Sha
F, Riahi K, Meinshausen M (2016) Paris Agreement climate proposals need aboost to keep warming well below 2 °C. Nature 534:631. https://doi.org/10.
1038/nature18307

Wang and Zhang Progress in Earth and Planetary Science(2020) 7:11

Rohli RV, Andrew Joyner T, Reynolds SJ, Shaw C, Vázquez JR (2015) Globallyextended Kӧppen–Geiger climate classification and temporal shifts interrestrial climatic types. Phys Geogr 36(2):142–157. https://doi.org/10.1080/
02723646.2015.1016382
Schleussner C-F, Lissner TK, Fischer EM, Wohland J, Perrette M, Golly A, Rogelj J,
Childers K, Schewe J, Frieler K, Mengel M, Hare W, Schaeffer M (2016)
Differential climate impacts for policy-relevant limits to global warming: thecase of 1.5 &deg;C and 2 &deg;C. Earth Syst Dynamics 7(2):327–351. https://doi.org/10.5194/esd-7-327-2016
Sherwood S, Qiang F (2014) A drier future? Science (New York, N.Y.) 343:737–739.
https://doi.org/10.1126/science.1247620
Sylla MB, Elguindi N, Giorgi F, Wisser D (2016) Projected robust shift of climate
zones over West Africa in response to anthropogenic climate change for thelate 21st century. Clim Chang 134(1):241–253. https://doi.org/10.1007/s10584015-1522-z
Taylor KE, Stouffer RJ, Meehl GA (2011) An overview of CMIP5 and theexperiment design. Bull Am Meteorol Soc 93(4):485–498. https://doi.org/10.
1175/BAMS-D-11-00094.1
Trenberth KE, Dai A, van der Schrier G, Jones PD, Barichivich J, Briffa KR, Sheffield
J (2013) Global warming and changes in drought. Nat Clim Chang 4:17.
https://doi.org/10.1038/nclimate2067
UNFCCC. (2015a). Adoption of the Paris Agreement: proposal by the president.
Edited by FCCC/CP/2015/L.9/rev.1. Geneva
UNFCCC. (2015b). Synthesis report on the aggregate effect of the intended
nationally determined contributions. Edited by FCCC/CP/2015/7. Paris
UNFCCC. (2019). National Inventory Submissions
van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC,
Kram T, Krey V, Lamarque JF, Masui T, Meinshausen M, Nakicenovic N, Smith
SJ, Rose SK (2011) The representative concentration pathways: an overview.
Clim Chang 109(1–2):5–31. https://doi.org/10.1007/s10584-011-0148-z
Wang F, Tokarska KB, Zhang J, Ge Q, Hao Z, Zhang X, Maowei W (2018) Climate
warming in response to emission reductions consistent with the Paris
Agreement. Adv Meteorol 2018:1–9. https://doi.org/10.1155/2018/2487962
Wang L, Chen W (2014) A CMIP5 multimodel projection of future temperature,
precipitation, and climatological drought in China. Int J Climatology 34.
https://doi.org/10.1002/joc.3822
Wang L, Chen W, Huang G, Zeng G (2017) Changes of the transitional climatezone in East Asia: past and future. Clim Dyn 49(4):1463–1477. https://doi.org/
10.1007/s00382-016-3400-4
Yang J, Ding Y, Chen R, Liu L (2003) The fluctuation of dry and wet climateboundary and its causal analyses in China. Acta Meteorologica Sinica 61(3):364–374. https://doi.org/10.11676/qxxb2003.035
Yang Q, Li MX, Zheng ZY, Ma ZG (2017) Regional applicability of sevenmeteorological drought indices in China. Science China Earth Sciences 60(4):745–760. https://doi.org/10.1007/s11430-016-5133-5
Yin Y, Ma D, Wu S, Pan T (2015) Projections of aridity and its regional variability
over China in the mid-21st century. Int J Climatol 35(14):4387–4398. https://doi.org/10.1002/joc.4295
Zhang J, Wang F (2019) Extreme precipitation in China in response to emission
reductions under the Paris Agreement. Water 11(6). https://doi.org/10.3390/
w11061167

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