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[Geophysical Research Letters] 2

Supporting Information for 3

Global land monsoon precipitation changes in CMIP6 projections 4

Ziming Chen1,2, Tianjun Zhou1,2,3, Lixia Zhang1,3, Xiaolong Chen1, Wenxia Zhang1, Jie 5

Jiang1,2 6

1 LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 7

2 University of Chinese Academy of Sciences, Beijing 100049, China 8

3 CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences (CAS), Beijing 9

100101, China 10

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Contents of this file 13

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Text S1 15

Figures S1 to S7 16

Tables S1 to S4 17

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Text S1. Moisture budget analysis 20

To understand the changes and uncertainty in the projections of global monsoon 21

precipitation, the moisture budget analysis is used, following Chou et al. (2009). 22

The vertically integrated anomalous moisture equation is written as 23

𝑃′ = 𝐸′ − 𝜕𝑡⟨𝑞⟩′ − ⟨𝑢ℎ⃗⃗ ⃗⃗ ∙ ∇ℎ𝑞⟩′ − ⟨𝜔𝜕𝑝𝑞⟩′, (1) 24

where the angle brackets indicate the mass integral through the entire troposphere, and 25

the primes denote the changes relative to the baseline climatology. 𝑞 denotes specific 26

humidity; 𝑢ℎ⃗⃗ ⃗⃗ and 𝜔 denote horizontal wind and vertical pressure velocity, 27

respectively; 𝐸 and 𝑃 are evaporation and precipitation, respectively. On the 28

seasonal mean time scale, the time derivative of specific humidity, 𝜕𝑡⟨𝑞⟩′ , can be 29

ignored since it is much smaller than other terms. −⟨𝑢ℎ⃗⃗ ⃗⃗ ∙ ∇ℎ𝑞⟩′ represents horizontal 30

moisture advection, and −⟨𝜔𝜕𝑝𝑞⟩′ represents vertical moisture advection, which can 31

be further divided as follows: 32

−⟨𝑢ℎ⃗⃗ ⃗⃗ ∙ ∇ℎ𝑞⟩′ = −⟨𝑢ℎ⃗⃗ ⃗⃗ ̅̅ ̅ ∙ ∇𝑞′⟩ − ⟨𝑢ℎ⃗⃗ ⃗⃗ ′∙ ∇�̅�⟩ − ⟨𝑢ℎ⃗⃗ ⃗⃗

′∙ ∇𝑞′⟩, (2) 33

−⟨𝜔𝜕𝑝𝑞⟩′ = −⟨�̅�𝜕𝑝𝑞′⟩ − ⟨𝜔′𝜕𝑝�̅�⟩ − ⟨𝜔′𝜕𝑝𝑞

′⟩, (3) 34

where ( ̅) denotes the baseline climatology. The thermodynamic terms in the vertical 35

and horizontal moisture advection are −⟨�̅�𝜕𝑝𝑞′⟩ and −⟨𝑢ℎ⃗⃗ ⃗⃗ ̅̅ ̅ ∙ ∇𝑞′⟩, respectively, while 36

the dynamic terms are −⟨𝜔′𝜕𝑝�̅�⟩ and −⟨𝑢ℎ⃗⃗ ⃗⃗ ′∙ ∇�̅�⟩ , respectively. −⟨𝑢ℎ⃗⃗ ⃗⃗

′∙ ∇𝑞′⟩ and 37

−⟨𝜔′𝜕𝑝𝑞′⟩ are horizontal and vertical non-linear terms, respectively, and their sum 38

denotes as NL. Therefore, the changes in precipitation can be expressed as: 39

𝑃′ = 𝐸′ − ⟨𝑢ℎ⃗⃗ ⃗⃗ ̅̅ ̅ ∙ ∇𝑞′⟩ − ⟨𝑢ℎ⃗⃗ ⃗⃗ ′∙ ∇�̅�⟩ − ⟨�̅�𝜕𝑝𝑞

′⟩ − ⟨𝜔′𝜕𝑝�̅�⟩ + 𝑁𝐿 + 𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙, (4) 40

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Figure S1. Changes of global land summer monsoon precipitation in historical climate 44

simulation and four SSPs projections of 19 CMIP6 models. Each line in each SSP 45

represents the multi-model ensemble (MME). Changes are relative to the 1995-2014 46

mean. Time series are normalized by the climate mean values and smooth with a 10-yr 47

running mean filter (Unit: %). For each model, the area-mean changes of global land 48

monsoon precipitation are calculated and then normalized by the climate mean values 49

in the original resolution before MME. The bars represent the MME and uncertainty in 50

the 2080-2099. The black solid, dash and dot lines are the observational series from the 51

Climatic Research Unit (CRU) Time-Series (TS) version 4.02 (0.5°x0.5°; Harris et al., 52

2014), Global Precipitation Climatology Centre version 7 (GPCC v7, 0.5 ° x0.5° ; 53

Schneider et al., 2017), Precipitation Reconstruction over Land (PREC/L, 1°x1°; Chen 54

et al., 2002) and the University of Delaware Air Temperature and Precipitation version 55

5.01 (UDel v5.01, 0.5°x0.5°; Willmott and Matsuura, 2001). 56

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Figure S2. The changes of moisture budget terms in the long-term averaged over 7 sub-61

monsoon and the global land monsoon domain relative to 1995-2014. The bars 62

represent the MME, while the vertical lines indicate the range of 10th to 90th. Unit: 63

𝑚𝑚 𝑑𝑎𝑦−1. 64

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Figure S3. Relationship of precipitation changes to −⟨𝜔′𝜕𝑝�̅�⟩ (DY) over global land 69

monsoon domain (red box) and sub-monsoon regions in near-term (blue), mid-term 70

(yellow) and long-term (red) projection with the correlation coefficient in same color. 71

The ** indicates correlation or regression pass the 95% confidence level. Units: %. 72

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Figure S4. Relationship between global mean surface air temperature (GMSAT) 76

changes and −⟨�̅�𝜕𝑝𝑞′⟩ percentage changes in summer over global land monsoon 77

regions under SSP5-8.5 scenario. The percentage of −⟨�̅�𝜕𝑝𝑞′⟩ are normalized by the 78

climatology of −⟨�̅�𝜕𝑝�̅�⟩ for the period of 1995-2014. The solid lines indicate the 79

linear fit, with the regression coefficient (% 𝐾−1) shown at legends. The ** indicates 80

correlation or regression significant at the 99% confidence level. The results in near-, 81

mid- and long-terms are in blue, yellow and red, respectively. Units: K to GMSAT and % 82

to others. 83

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Figure S5. Same as Figure S3 but for the relationship between global mean surface air 87

temperature (GMSAT) changes and the sum of −⟨𝜔′𝜕𝑝�̅�⟩ and non-linear term (NL) 88

over global land monsoon region. 89

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Figure S6. Relationship between precipitation changes and −⟨�̅�𝜕𝑝𝑞′⟩ (a), and 93

−⟨𝜔′𝜕𝑝�̅�⟩ (b) in AMIP-p4K run. The change percentages are relative to the 94

climatological precipitation in AMIP run. The solid lines indicate the linear fit, with 95

correlation coefficient (r) between precipitation changes and −⟨�̅�𝜕𝑝𝑞′⟩ (a), and 96

−⟨𝜔′𝜕𝑝�̅�⟩ (b). The standard deviations (STD) of −⟨�̅�𝜕𝑝𝑞′⟩ (a), and −⟨𝜔′𝜕𝑝�̅�⟩ (b) 97

are also shown. The ** indicates correlation significant at the 99% confidence level. 98

Units: %. 99

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Figure S7. Relationship between the changes of global mean surface air temperature 104

(GMSAT, units: K) and GLM summer −⟨𝜔′𝜕𝑝�̅�⟩ (units: %) in the near- (a, 2021-105

2040), mid- (b, 2041-2060) and long- (c, 2080-2099) term projection under SSP5-8.5 106

scenario. Each red scatter represents an individual CMIP6 model. The purple, black and 107

blue scatters are the results of IPSL-CM6A-LR, CanESM5 and UKESM1-0-LL each 108

of which has multi-realizations. The numbers in the parenthesis of the legends are the 109

number of realizations. The diamonds are the multi-model ensemble (MME) and the 110

multi-member ensemble in the same model. The horizontal and vertical lines are the 111

±1 standard deviation of GMSAT and −⟨𝜔′𝜕𝑝�̅�⟩ across models (red) and realizations 112

of the same model (other colors), respectively. 113

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Table S1. Information of 19 CMIP6 models. Analyses related to SSP1-2.6 and SSP3-115

7.0 are based on 18 of 19 models excluding GFDL-CM4, and 17 of 19 models excluding 116

GFDL-CM4 and NESM3, respectively, since these experiments are unavailable in these 117

models. Moisture budget is based on 18 of 19 models excluding MCM-UA-1-0, as the 118

vertical velocity in this model is unavailable. 119

Model Institute/Country Lat x Lon The Number of

Realizations

BCC-CSM2-MR BCC-CMA/China 160 x 320 1

CAMS-CSM1-0 CAMS-CMA/China 160 x 320 1

CNRM-CM6-1 CNRM-CERFACS/France 128 x 256 1

CNRM-ESM2-1 CNRM-CERFACS/France 128 x 256 1

CanESM5 CCCMA/Canada 64 x 128 6

EC-Earth3 EC-Earth-Consortium/EU 256 x 512 1

EC-Earth3-Veg EC-Earth-Consortium/EU 256 x 512 1

FGOALS-f3-L LASG-IAP/China 180 x 360 1

FGOALS-g3 LASG-IAP/China 90 x 180 1

GFDL-CM4 GFDL-NOAA/USA 180 x 360 1

GFDL-ESM4 GFDL-NOAA/USA 180 x 360 1

INM-CM5-0 INM/Russia 120 x 180 1

IPSL-CM6A-LR IPSL/France 143 x 144 6

MCM-UA-1-0 UA/USA 80 x 96 1

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MIROC6 MIROC/Japan 128 x 256 1

MIROC-ES2L MIROC/Japan 64 x 128 1

MRI-ESM2-0 MRI/Japan 96 x 192 1

NESM3 NUIST/China 96 x 192 1

UKESM1-0-LL MOHC/UK 144 x 192 5

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Table S2. Information of 9 CMIP6 models used in the Atmospheric Model 121

Intercomparison Projection (AMIP) and AMIP-p4K experiments. 122

Model Institute/Country Lat x Lon The Number

of Realizations

BCC-CSM2-MR BCC-CMA/China 160 x 320 1

CESM2-CAM6 NCAR/USA 192 x 288 1

CNRM-CM6-1 CNRM-CERFACS/France 128 x 256 1

CanESM5 CCCMA/Canada 64 x 128 1

GFDL-CM4 GFDL-NOAA/USA 180 x 360 1

HadGEM3-GC31-LL MOHC/UK 144 x 192 1

IPSL-CM6A-LR IPSL/France 143 x 144 1

MIROC6 MIROC/Japan 128 x 256 1

MRI-ESM2-0 MRI/Japan 96 x 192 1

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Table S3. A brief description of the AMIP and AMIP-p4K experiments. 126

Experiment Description Time range

AMIP

An atmospheric model is driven by the prescribe

observed sea surface temperature (SST) and sea

ice concentrations. Other conditions are same as

the Historical run (Eyring et al., 2016). The mean

of the output results for the period of 1995-2014

are as the control values for AMIP-p4K.

1979-2014

AMIP-p4K

Same as AMIP run but the SST are subject to a

uniform warming of 4 K (Webb et al., 2017). The

difference between AMIP-p4K and AMIP in

1995-2014 are used to represent the effects of

uniform warming.

1979-2014

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Table S4. The MME of summer precipitation changes rate over global land monsoon 129

and sub-monsoon in near-term (2021-2040), mid-term (2041-2060) and long-term 130

(2080-2099) projection under SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5 scenarios, 131

relative to 1995-2014. The range in the parenthesis are the spread from 10th to 90th. 132

Units: %. 133

Term SSP1-2.6 SSP2-4.5 SSP3-7.0 SSP5-8.5

Global Land

Monsoon

Near 1.76 (0.19 ~ 3.33) 1.33 (-0.64 ~ 3.30) 0.96 (-1.08 ~ 2.99) 1.71 (-0.67 ~ 4.10)

Mid 2.29 (-0.10 ~ 4.69) 2.43 (0.06 ~ 4.80) 1.98 (-0.90 ~ 4.86) 3.04 (-0.01 ~ 6.08)

Long 2.54 (0.32 ~ 4.76) 3.52 (0.47 ~ 6.58) 3.51 (-1.46 ~ 8.49) 5.75 (-0.17 ~ 11.68)

NH Land

Monsoon

Near 2.73 (-0.60 ~ 6.06) 2.08 (-0.91 ~ 5.07) 1.16 (-2.21 ~ 4.53) 2.74 (-0.91 ~ 6.39)

Mid 3.69 (-0.64 ~ 8.02) 3.58 (0.35 ~ 6.81) 2.78 (-1.52 ~ 7.08) 4.48 (-0.38 ~ 9.33)

Long 4.29 (0.31 ~ 8.28) 5.50 (0.57 ~ 10.43) 5.53 (-1.07 ~ 12.13) 8.82 (0.53 ~ 17.11)

SH Land

Monsoon

Near 0.93 (-1.07 ~ 2.92) 0.73 (-2.62 ~ 4.07) 0.89 (-2.13 ~ 3.91) 0.86 (-2.59 ~ 4.30)

Mid 1.14 (-2.78 ~ 5.07) 1.43 (-2.31 ~ 5.17) 1.27 (-3.17 ~ 5.71) 1.78 (-2.75 ~ 6.31)

Long 0.96 (-1.86 ~ 3.79) 1.72 (-2.53 ~ 5.97) 1.65 (-5.64 ~ 8.93) 2.84 (-5.25 ~ 10.93)

East Asia

Near 3.79 (-1.89 ~ 9.46) 2.99 (-1.14 ~ 7.11) 1.46 (-3.59 ~ 6.51) 3.94 (-0.81 ~ 8.70)

Mid 6.24 (-0.54 ~13.02) 6.08 (1.30 ~ 10.86) 3.89 (-2.24 ~ 10.03) 7.10 (0.27 ~ 13.93)

Long 8.36 (0.41 ~16.31) 9.94 (1.46 ~ 18.42) 9.69 (0.54 ~ 18.83) 14.03 (2.63 ~ 25.42)

South Asia

Near 3.05 (-0.36 ~ 6.45) 1.92 (-0.83 ~ 4.68) 1.07 (-2.55 ~ 4.70) 2.17 (-1.97 ~ 6.32)

Mid 5.13 (0.53 ~ 9.74) 4.41 (1.38 ~ 7.44) 3.46 (-1.86 ~ 8.79) 5.94 (0.24 ~ 11.64)

Long 6.21 (2.38 ~10.04) 8.20 (2.78 ~ 13.62) 10.95 (1.24 ~ 20.67) 18.21 (7.18 ~ 29.23)

North Africa

Near 3.06 (-2.36 ~ 8.48) 3.31 (-2.21 ~ 8.83) 3.06 (-2.91 ~ 9.04) 5.72 (-1.96 ~ 13.39)

Mid 3.01 (-3.13 ~ 9.14) 4.08 (-1.89 ~ 10.05) 5.72 (-2.58 ~ 14.01) 7.03 (-2.53 ~ 16.59)

Long 2.45 (-3.52 ~ 8.43) 4.93 (-3.08 ~ 12.94) 7.87 (-5.15 ~ 20.90) 8.99 (-6.95 ~ 24.93)

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NH America

Near 0.30 (-4.66 ~ 5.25) -0.43 (-5.36 ~ 4.50) -2.12 (-6.42 ~ 2.17) -1.62 (-6.99 ~ 3.75)

Mid -0.84 (-6.57 ~ 4.89) -1.62 (-7.65 ~ 4.42) -3.84 (-10.48 ~ 2.79) -5.01 (-12.24 ~ 2.22)

Long -0.95 (-7.21 ~ 5.32) -3.49 (-10.56 ~ 3.58) -11.56 (-25.45 ~ 2.32) -13.62 (-27.68 ~ 0.43)

SH America

Near 0.87 (-1.40 ~ 3.14) 0.47 (-3.25 ~ 4.18) 0.50 (-2.86 ~ 3.85) -0.15 (-4.51 ~ 4.22)

Mid 1.21 (-3.81 ~ 6.22) 0.75 (-3.82 ~ 5.31) -0.03 (-6.18 ~ 6.11) 0.26 (-5.22 ~ 5.73)

Long 0.38 (-3.30 ~ 4.06) 0.27 (-6.20 ~ 6.73) -0.60 (-10.01 ~ 8.81) -0.49 (-11.81 ~ 10.84)

South Africa

Near 0.56 (-3.19 ~ 4.31) 0.99 (-3.17 ~ 5.15) 0.91 (-3.34 ~ 5.17) 1.69 (-2.12 ~ 5.51)

Mid 0.73 (-4.36 ~ 5.82) 1.80 (-2.43 ~ 6.04) 2.13 (-3.31 ~ 7.57) 2.66 (-2.37 ~ 7.69)

Long 1.11 (-2.58 ~ 4.81) 3.11 (-2.03 ~ 8.26) 2.82 (-4.88 ~ 10.53) 5.48 (-3.14 ~ 14.10)

Australia

Near 1.60 (-3.91 ~ 7.11) 0.65 (-6.21 ~ 7.52) 2.18 (-3.78 ~ 8.14) 2.22 (-3.14 ~ 7.59)

Mid 1.49 (-4.08 ~ 7.05) 2.45 (-4.20 ~ 9.10) 3.49 (-2.47 ~ 9.45) 4.85 (-3.33 ~ 13.03)

Long 2.21 (-4.57 ~ 8.98) 3.04 (-4.43 ~ 10.51) 7.16 (-3.49 ~ 17.81) 8.54 (-2.11 ~ 19.18)

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