The future of droughts in Iran according to CMIP6 projections

Figures & data

Figure 1. The study area and synoptic station clusters.

Table 1. Information on the AR6 (The Sixth Assessment Report) climate models used in this study.

Model number	Model	Institute	Resolution (Long. × Lat.)	Reference
1	ACCESS-CM2	Australian Community Climate and Earth System Simulator (ACCESS)	1.875 × 1.25	Bi et al. (2020)
2	ACCESS-ESM1-5	Australian Community Climate and Earth System Simulator (ACCESS)	1.875 × 1.25	Ziehn et al. (2020)
3	AWI-ESM-1-1-LR	Alfred Wegener Institute (AWI)	1.88 × 1.88	Shi et al. (2020)
4	BCC-CSM2-MR	Beijing Climate Center (BCC) and China Meteorological Administration (CMA)	1.13 × 1.13	Wu et al. (2018)
5	BCC-ESM1	Beijing Climate Center (BCC) and China Meteorological Administration (CMA)	2.81 × 2.81	Zhang (2018)
6	CanESM5	Canadian Earth System Model	2.81 × 2.81	Swart et al. (2019)
7	CESM2	National Center for Atmospheric Research	1.25 × 0.94	Akinsanola et al. (2021)
8	CESM2-FV2	National Center for Atmospheric Research (NCAR)	1.9 x 2.5	Danabasoglu et al. (2019)
9	CESM2-WACCM	National Center for Atmospheric Research	1.25 × 0.94	Liao et al. (2021)
10	CMCC-CM2-HR4	Centro Euro-Mediterraneo sui Cambiamenti Climatici	1.25 × 1.875	Scoccimarro et al. (2022)
11	CMCC-CM2-SR5	Centro Euro-Mediterraneo sui Cambiamenti Climatici	1.25 × 1.875	Lovato and Peano (2020)
12	CMCC-ESM2	Centro Euro-Mediterraneo sui Cambiamenti Climatici	1.25 × 1.875	Peano et al. (2020)
13	CNRM-CM6-1	Centre National de Recherches Météorologiques (CNRM); Centre Européen de Recherches et de Formation Avancéen Calcul Scientifique	1.41 × 1.41	Voldoire et al. (2019)
14	CNRM-CM6-1-HR	Centre National de Recherches Météorologiques, Toulouse 31 057, France	0.5 × 0.5	Xin et al. (2021)
15	CNRM-ESM2-1	Centre National de Recherches Météorologiques, Toulouse 31 057, France	1.41 × 1.41	Lim Kam Sian et al. (2021)
16	EC-Earth3-AerChem	The State Meteorological Agency (AEMET) Spain	3 × 2	van Noije et al. (2021)
17	EC-Earth3-CC	The State Meteorological Agency (AEMET) Spain	3 × 2	EC-Earth (2019)
18	EC-Earth3-Veg-LR	The State Meteorological Agency (AEMET) Spain	0.7 × 0.7	Lim Kam Sian et al. (2021)
19	FGOALS-f3-L	Chinese Academy of Sciences (CAS) Flexible Global Ocean–Atmosphere–Land System	2.81 × 1.66	He et al. (2019)
20	FGOALS-g3	Chinese Academy of Sciences (CAS) Flexible Global Ocean–Atmosphere–Land System	2 × 2.25	Lim Kam Sian et al. (2021)
21	GFDL-ESM4	Geophysical Fluid Dynamics Laboratory (GFDL)	1.25 × 1.00	Krasting et al. (2018)
22	HadGEM3-GC31-LL	Met Office Hadley Centre	1.86 × 1.25	Ridley et al. (2019)
23	HadGEM3-GC31-MM	Met Office Hadley Centre	0.54 × 0.83	Xin et al. (2021)
24	IITM-ESM	Indian Institute of Tropical Meteorology Earth System Models	1.875 × 3.83	Raghavan and Panickal (2019)
25	INM-CM4-8	Institute for Numerical Mathematics, Russian Academy of Science/Russia	2 × 1.5	Lim Kam Sian et al. (2021)
26	INM-CM5-0	Institute for Numerical Mathematics, Russian Academy of Science/Russia	2 × 1.5	Lim Kam Sian et al. (2021)
27	IPSL-CM5A2-INCA	Institute Pierre-Simon Laplace (IPSL)	3.75 × 1.875	Sepulchre et al. (2020)
28	IPSL-CM6A-LR	Institute Pierre-Simon Laplace (IPSL)	2.50 × 1.26	Boucher et al. (2018)
29	KACE-1-0-G	National Institute of Meteorological Sciences/Korea Meteorological Administration, Climate Research Division, Korea	1.875 × 2.5	Byun et al. (2019)
30	KIOST-ESM	Korea Institute of Ocean Science & Technology	1.875 × 3.75	Pak et al. (2021)
31	MIROC6	Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute, National Institute for Environmental Studies, RIKEN Center for Computational Science	1.4 × 1.4	Lim Kam Sian et al. (2021)
32	MIROC-ES2L	Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute, National Institute for Environmental Studies, RIKEN Center for Computational Science	2.8 × 2.8	Lim Kam Sian et al. (2021)
33	MPI-ESM1-2-HR	Max Planck Institute for Meteorology/Germany	0.935 × 0.935	Xin et al. (2021)
34	MPI-ESM1-2-LR	Max Planck Institute for Meteorology/Germany	1.875 × 1.875	Lim Kam Sian et al. (2021)
35	MRI-ESM2-0	Meteorological Research Institute (MRI)	1.13 × 1.13	Yukimoto et al. (2019)
36	NESM3	Nanjing University of Information Science and Technology	1.875 × 1.875	Lim Kam Sian et al. (2021)
37	NorCPM1	Norwegian Climate Centre	2.5 × 3.75	Bethke et al. (2019)
38	NorESM2-MM	Norwegian Climate Centre	1.25 × 0.9374	Lim Kam Sian et al. (2021)
39	SAM0-UNICON	Seoul National University Atmosphere Model Version 0 with a Unified Convection Scheme	1.25 × 0.94	Park and Shin (2019)
40	TaiESM1	Research Center for Environmental Changes, Academia Sinica, Taiwan	1.25 × 0.9	Wang et al. (2021)
41	UKESM1-0-LL	Met Office Hadley Centre	1.88 × 1.25	Tang et al. (2023)

Table 2. Summary of SSP (The Shared Socio-economic Pathway) narratives.

SSP element	SSP1	SSP5
Economy and lifestyle:
Growth (per capita)	High in low-income countries (LICs), medium in high-income countries (HICs)	High
Inequality	Reduced across and within countries	Strongly reduced, especially across countries
International trade	Moderate	High, with regional specialization
Globalization	Connected markets, regional production	Strongly globalized, increasingly connected
Consumption and diet	Low growth in material consumption, low-meat diets, first in HICs	Materialism, status, consumption, tourism, mobility, meat-rich diets
Policies and institutions:
International cooperation	Effective	Effective in pursuit of development goals, more limited for environmental goals
Environmental policy	Improved management of local and global issues; tighter regulation of pollutants	Focus on local environment with obvious benefits to well-being, little concern with global problems
Policy orientation	Towards sustainable development	Towards development, free markets, human capital
Institutions	Effective at national and international levels	Increasingly effective, oriented towards fostering competitive markets
Technology:
Development	Rapid	Rapid
Transfer	Rapid	Rapid
Energy tech change	Directed away from fossil fuels, towards efficiency and renewables	Directed towards fossil fuels; alternative sources not actively pursued
Carbon intensity	Low	High
Energy intensity	Low	High
Environment and natural resources:
Fossil constraints	Preferences shift away from fossil fuels	None
Environment	Improving conditions over time	Highly engineered approaches, successful management of local issues
Land use	Strong regulations to avoid environmental tradeoffs	Medium regulations lead to slow decline in the rate of deforestation
Agriculture	Improvements in agricultural productivity; rapid diffusion of best practices	Highly managed, resource-intensive; rapid increase in productivity

Table 3. List of extreme precipitation indices used in this study.

S/N	Indices	Name	Definition	Unit
1	SDII	Simple daily intensity	Let $P_{\mathit{w},\mathit{y}}$ be the daily precipitation amount on wet days, $P_w\ge 1$ mm in period $\mathit{y}$. If $\mathit{W}$ represents the number of wet days in y, then: $\mathit{SDI}{I}_y=\frac{{\sum }_{w=1}^W{P}_{\mathit{w},\mathit{y}}}{\mathit{W}}$	mm/ day
2	CDD	Consecutive dry days	Let $P_{\mathit{d},\mathit{y}}$ be the daily precipitation amount on day $\mathit{d}$ in period $\mathit{y}$. Count the largest number of consecutive days where $P_{\mathit{d},\mathit{y}}<1$ mm	day
3	CWD	Consecutive wet days	Let $P_{\mathit{d},\mathit{y}}$ be the daily precipitation amount on day $\mathit{d}$ in period $\mathit{y}$. Count the largest number of consecutive days where $P_{\mathit{d},\mathit{y}}\ge 1$ mm	day
4	PRCPTOT	Total wet-day precipitation	Let $P_{\mathit{d},\mathit{y}}$ be the daily precipitation amount on day $\mathit{d}$ in period $\mathit{y}$. If $\mathit{D}$ represents the number of days in y, then: $\mathit{PRCPTO}{T}_y=\sum _{\mathit{d}=1}^D{P}_{\mathit{d},\mathit{y}}$	mm
5	R10mm	Heavy precipitation days	Let $P_{\mathit{d},\mathit{y}}$ be the daily precipitation amount on day $\mathit{d}$ in period $\mathit{y}$. Count the number of days where $P_{\mathit{d},\mathit{y}}\ge 10$ mm	day
6	R20mm	Very heavy precipitation days	Let $P_{\mathit{dy}}$ be the daily precipitation amount on day $\mathit{d}$ in period $\mathit{y}$. Count the number of days where $P_{\mathit{d},\mathit{y}}\ge 20$ mm	day
7	RX5day	Max 5-day precipitation	Let $P_{\mathit{d},\mathit{y}}$ be the daily precipitation amount on day $\mathit{d}$ in period $\mathit{y}$. If $\mathit{D}$ represents the number of days in y, then: $\mathit{RX}5\mathit{da}{y}_y=\mathit{Max}\left\{\left(P_{\mathit{d},\mathit{y}}+P_{\mathit{d}-1,y}+P_{\mathit{d}-2,\mathit{y}}+P_{\mathit{d}-3,y}+P_{\mathit{d}-4,y}\right)\right\}$ and $\mathit{d}\in \left\{5,6,7,..,\mathit{D}\right\}$	mm
8	R95pTOT	Very wet days	Let $P_{\mathit{w},\mathit{y}}$ be the daily precipitation amount on wet days, $P_w\ge 1$ mm in period $\mathit{y},$ and let $\mathit{Pwn}95$ be the 95th percentile of precipitation on wet days in the 1986–2014 period (observation period). If W represents the number of wet days in y, then: $\mathrm{R}95\mathrm{pTO}{\mathrm{T}}_y=\sum _{\mathit{w}=1}^W{P}_{\mathit{w},\mathit{y}}$, where $P_{\mathit{w},\mathit{y}}>\mathit{Pwn}95$	mm
9	R99pTOT	Extremely wet days	Let $P_{\mathit{w},\mathit{y}}$ be the daily precipitation amount on wet days, $P_w\ge 1$ mm in period $\mathit{y},$ and let $\mathit{Pwn}99$ be the 99th percentile of precipitation on wet days in 1986–2014 period (observation period). If W represents the number of wet days in y, then: $\mathrm{R}95\mathrm{pTO}{\mathrm{T}}_y=\sum _{\mathit{w}=1}^W{P}_{\mathit{w},\mathit{y}}$, where $P_{\mathit{w},\mathit{y}}>\mathit{Pwn}99$	mm

Figure 2. Root mean square relative error (RMSRE) (top) and decreasing sorted sum of RMSRE (bottom) for the nine extreme precipitation indices (EPIs) for 41 GCMs.

Figure 3. Percent bias (PBias) (top) and decreasing sorted sum of the absolute PBias (bottom) for the nine EPIs for 41 GCMs.

Figure 4. Annual precipitation projected by GCMs (top: ACCESS-CM2, bottom: INM-CM4-8) during the future period (2023–2099) under climate change scenarios SSP1-2.6 (left) and SSP5-8.5 (right).

Figure 5. MME (The Multi-Model Ensemble) projected precipitation variation during the future period (2023–2099) under climate change scenarios SSP1-2.6 (left) and SSP5-8.5 (right).

Figure 6. Precipitation variation based on SSP1-2.6 and a multi-model ensemble (ACCESS-CM2 and INM-CM4-8) during the future period (2023–2099). Each plot is for a representative station of a particular cluster.

Figure 6. (Continued).

Figure 7. Precipitation variation based on SSP5-8.5 and a multi-model ensemble (ACCESS-CM2 and INM-CM4-8) during the future period (2023–2099). Each plot is for a representative station of a particular cluster.

Figure 7. (Continued).

Figure 8. The effect of extreme values on the observed average annual precipitation during the future period (2023–2099).

Figure 9. The effect of extreme values on the average annual precipitation during the future period (2023–2099) for SSP5-8.5.

Figure 10. The effect of extreme values on the average annual precipitation during the future period (2023–2099) for SSP1-2.6.

Table 4. The best-fitted distributions for copulas.

Cluster ID	Cluster indicant station	The best-fitted distributions
		SSP1-2.6	SSP5-8.5
Cluster #1	Chalus	Gumbel	Frank
Cluster #2	Ahvaz	Frank	Frank
Cluster #3	Kabudarahang	Frank	Gumbel
Cluster #4	Tabas	Gumbel	Frank
Cluster #5	Minab	Gumbel	Gumbel

Figure 11. The joint drought return period due to SSP1-2.6 and multi-model ensemble (ACCESS-CM2 and INM-CM4-8).

Figure 12. The joint drought return period due to SSP5-8.5 and multi-model ensemble (ACCESS-CM2 and INM-CM4-8).

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Data availability statement

All data described in the main text are available. The observed dataset of synoptic stations is available from Iran’s meteorological organization website (https://data.irimo.ir/). All climate model simulations are from CMIP6 and are publicly available, and are hosted on various servers including the Copernicus datasets (https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cmip6?tab=form).

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