Compounding future escalation of emissions- and irrigation-induced increases in humid-heat stress

Domain: earth_science
Published: 2025-10-22
Editorial outcome: minor_revision

Abstract
Irrigation has been investigated as an important historical climate forcing, but there is no study exploring its future climatic impacts considering possible changes in both extent and efficiency. Here, we address these issues via developing irrigation efficiency scenarios in line with the Shared Socio-economic Pathways (SSPs), implementing these in the Community Earth System Model, and applying them to generate projections over the period 2015–2074. We project that annual irrigation water withdrawal decreases under SSP1-2.6 (from ~2100 to ~1700 km3 yr−1) but increases under SSP3-7.0 (to ~2400 km3 yr−1), with some new irrigation hotspots emerging, especially in Africa. Irrigation is projected to reduce the occurrence of dry-heat stress under both scenarios, but cannot reverse the warming trend due to greenhouse gas emission (e.g., increasing from ~90 to around 600 and 1200 hours yr−1 in intensely irrigated areas, under two scenarios). Moreover, moist-heat extreme event frequency increases more substantially (by ≥1600 hours yr−1 under SSP3-7.0 in tropical regions), and irrigation further amplifies the hours of exposure (for example, by ≥100 hours yr−1 in South Asia), thereby raising the risk of moist-heat-related illnesses and mortality for exposed communities. Our results underscore the importance of reducing greenhouse gas emissions, limiting irrigation expansion and improving irrigation efficiency to preserve water resources and decelerate escalating exposure to dry- and moist-heat stress.

Introduction
With increasing population size and improving life quality, the fibre demand, irrigation will likely continue to expand under area equipped for irrigation (AEI) has expanded substantially in most future scenarios. However, the few existing studies explor- the last century, from less than 1 × 106 km2 at the beginning of the ing the impacts of irrigation under future scenarios 8–10 all assume 20th century to ~3 × 106 km2 in 2015 1,2. Expanding irrigation that there are no changes in AEI or irrigation water flux relative to affects local climate conditions by changing the water and energy the present-day.

The water applied for irrigation increases surface and soil moisture in agricultural fields, causing evaporative cooling of the balance, making irrigation an increasingly important historical near-surface air 4,11. This cooling impact is generally more pronounced climate forcing 3–6. Owing to the projected growth in food and during heat extremes and, therefore, irrigation can alleviate these extremes 5,11–13. However, evaporated water also increases near-surface air humidity, which in turn dampens humans’ ability to cool down through sweating. As a result, irrigation can actually reduce the thermal comfort of people despite its cooling benefit 6,14,15. Yet, existing studies exploring irrigation’s climatic impacts under future scenarios focused solely on temperature, which may induce an over-optimism of irrigation’s cooling benefits for human beings.

With expanding AEI, irrigation water demand is also expected to increase, leading to water scarcity, soil salinisation, and other environmental issues 16–18. Thus, efficient irrigation techniques, like drip and sprinkler irrigation, are increasingly required to replace traditional flood irrigation systems. These techniques differ in the amount and way of applying water and therefore have varying impacts on the surface water cycle, energy balance, and climate 19,20. For example, remote sensing data show that water-saving irrigation has attenuated the cooling impacts of irrigation on surface temperature by 0.48 °C dec−1 in South Xinjiang, China 21, and idealised simulations reveal that switching from traditional flood to drip irrigation could slightly decrease wet-bulb temperature in North India 22. Considering transition pathways in irrigation techniques is therefore important when projecting irrigation-induced impacts on heat extremes. However, there is currently no global-scale dataset on projected shifts in irrigation methods. Previous efforts therefore, relied on an idealised annual growth in irrigation efficiency 23,24, or remained limited to a conceptual level 25.

Recent efforts have been made to include different irrigation techniques in the land component of Earth system models 19,20,26, and a dataset on spatial information about global irrigation techniques share (ITS) exists 27. These developments enable us to account for irrigation techniques in Earth system simulations. However, these newly developed irrigation modules have so far only been applied in land-only simulations, thereby ignoring irrigation-induced land-atmosphere feedbacks to the changes in surface water and energy budgets. Moreover, these models so far cannot implement multiple irrigation techniques for one crop type in one grid cell, nor update the fraction of different techniques to account for temporal changes.

To address these issues, we first develop a dataset of transient ITS under a range of Shared Socioeconomic Pathways-Representation Concentration Pathways (SSP-RCPs: see Methods) 28,29. Two SSP-RCPs, SSP1-2.6 (the green road, with low challenges for both mitigation and adaptation) and SSP3-7.0 (the rocky road, with both high challenges), are selected. Building on a country classification framework 30 and on available socioeconomic and climate projections, we estimate the fraction of irrigation techniques at the grid scale. A basic assumption is that countries with higher socio-economic capacities and drier climate conditions have more motivation to upgrade their irrigation system, which varies among scenarios. We subsequently implement these transient irrigation pathways in the Community Earth System Model version 2 (CESM2) with some developments that enable the model to update ITS information yearly (see Methods).

In this study, three irrigation techniques are considered for most crop types: drip irrigation, sprinkler irrigation, and flood irrigation. For rice, paddy irrigation is applied by default and is accounted for as flood irrigation in the generated ITS dataset. We then perform transient CESM2 simulations for the time period 1985–2074 under SSP1-2.6 and SSP3-7.0, once with and once without transient irrigation (three ensemble members for each experiment). This approach allows us to unravel the interacting impacts of anthropogenic climate forcing and irrigation transition pathways on local heat stress. Here, we focus on changes in irrigation water withdrawal (IWW) and irrigation-induced impacts on dry- and moist-heat stress.

Results
Irrigation water withdrawals highly depend on projected irrigation expansion and efficiency

The Land Use Harmonization version 2 (LUH2) dataset 31 shows that under SSP1-2.6, the global AEI remains nearly constant at around 2.7–2.8 × 106 km2, with a rapid increase in the projected use of efficient irrigation techniques according to our dataset (drip and sprinkler accounting for ~2/3 in 2100, up from ~1/9 in 2015) (Fig. 1a). Under SSP3-7.0, projected irrigated land 31 increases steadily, reaching more than 4 × 106 km2 by the end of the century, with a smaller fraction of drip and sprinkler techniques (less than 1/2 in 2100) (Fig. 1b).

Based on the spatial distribution of AEI and ITS (Figs. 2, 3, S1, and S2), we select 13 IPCC reference regions 32 with high irrigation activity or with varying changes in AEI under two scenarios (Fig. 4a), and pool them into three groups (Table S1). Group 1 includes regions (solid line in Fig. 2a) with higher socio-economic capacities, like Central North America and East Asia, enabling AEI to remain nearly constant and efficient irrigation to grow rapidly under both scenarios (slightly faster under SSP1-2.6, see Fig. 1c, d and S3). Group 2 consists of the regions (dashed line in Fig. 2a) with large AEI but lower socio-economic capacities in present day, such as South Asia and West Central Asia. In Group 2, AEI remains relatively constant under SSP1-2.6 but expands rapidly under SSP3-7.0 (Fig. 1e, f, and S4). The regions in Group 3 (dotted line in Fig. 2a) have almost no AEI historically and under SSP1-2.6, but their AEI experiences substantial expansion under SSP3-7.0, making them irrigation hotspot regions by the end of the century. These future hotspot regions, for example, include West and South Africa (Fig. 1g, h, and S5).

IWW is highly related to spatial and temporal changes in both AEI and ITS (Fig. 4). From 1985 to 2014, North India was the most intensely irrigated area with simulated IWW surpassing 500 mm yr−1 in some grid cells (over the entire 0.9° × 1.25° grid cell area). Other heavily irrigated areas included East China and Central USA, where simulated IWW also exceeds 300 mm yr−1 in several grid cells (Fig. 4a). Globally, IWW ranges between 1700–2000 km3 yr−1 during the historical period (in historical simulations, AEI and ITS are static at the level of the year 2010), and IWW projections start from around 2100 km3 yr−1 in the year 2015 (Fig. 4d).

IWW is projected to decrease in many grid cells over Central North America and East Asia under SSP1-2.6 (2045–2074) compared to the historical period (1985–2014), where the reduction exceeds 50 or even 100 mm yr−1 locally (Fig. 4b). Global IWW decreases slightly to ~1700 km3 yr−1 by 2074 (Fig. 4d), with the regional values falling from ~400 to ~300 km3 yr−1 in Group 1 (Fig. 4e and S6a–c), from ~1300 to ~1100 km3 yr−1 in Group 2 (Fig. 4f and S6d–h), and remaining constant at around 50 km3 yr−1 in Group 3 (Fig. 4g and S6i–m).

Under SSP3-7.0 (2045-2074), the reduction in IWW persists in Central North America and East Asia, but new irrigation hot spots appear across Africa, and IWW increases in some traditional irrigation hotspots like in South Asia (Fig. 4c). After 2015, IWW increases continuously to ~2400 km3 yr−1 under SSP3-7.0 (Fig. 4d). IWW shows different trends across the three region groups due to the varying socioeconomic developments in each group. In Group 1, IWW decreases to less than 300 km3 yr−1 (Fig. 4e and S6a–c), owing to a steady AEI and improvements in ITS (Fig. 1c, d and S3). In Group 2, IWW increases to ~1400 km3 yr−1 (Fig. 4f and S6d–h), mainly attributed to the expanding AEI (SSP3-7.0) (Fig. 1e, f and S4). In Group 3, a sharp increase from 50 to 400 km3 yr−1 in IWW is projected (Fig. 4g and S6i–m) due to a rapid AEI expansion (Fig. 1g, h and S5).

Divergent impacts of irrigation on dry- and moist-heat extremes

Changes in IWW affect irrigation-induced impacts on surface climate, and here we focus on dry- and moist-heat extremes. We define dry-heat extremes as events when the 3-hourly, 2-metre air temperature (T2m) exceeds the 99th percentile (that is, about the 30 warmest 3-hourly blocks, or 90 hours of a year) during the period 1985–2014 in the pooled simulations without irrigation (Fig. 5a). Without irrigation, South Asia, West Central Asia, and the southwestern part of the Sahara exhibit the most extreme temperatures, with the 99th percentile value of T2m reaching 45 °C over large areas. Other areas with high absolute values of dry-heat extremes include Central North America, North Central America, and some regions in South America and Australia.

Analogous to dry-heat extremes, we define moist-heat extremes as 3-hour periods when the wet-bulb temperature (Tw) exceeds the 99th percentile during the period 1985-2014 in the ensemble without irrigation (Fig. 5b). The regions experiencing the highest dry-heat temperatures also often face extreme moist-heat temperatures, such as North India and the western part of the Sahara, with a 99th percentile value of Tw exceeding 26 or even 28 °C.

Historical irrigation has cooling impacts on dry-heat extremes (Fig. 5c), reducing the exposure hours of this event in many areas in South Asia, East Asia, West Central Asia, and Central North America, by more than 25 hours yr−1 (28%). Unlike its impacts on dry-heat extremes, historical irrigation slightly increases the frequency of moist-heat extremes, particularly in specific regions such as West Central Asia, the Mediterranean, and Southeast Asia, where their annual occurrence has increased by more than 25 hours (Fig. 5d). The cooling effects on dry-heat extremes and the warming effects on moist-heat extremes are both generally projected to amplify in future periods under SSP3-7.0. For instance, the annual exposure to dry-heat in certain grid cells in South Asia is projected to decline by ≥100 hours yr−1 due to irrigation (Fig. 5g). Likewise, in some grid cells in West Central Asia, the irrigation-induced increase in annual moist-heat exposure also surpasses 100 hours yr−1 (Fig. 5h). However, under SSP1-2.6, irrigation-induced impacts are expected to vary across regions. In areas with higher socio-economic capacity, such as Central and West North America, the effects of irrigation on dry-heat extremes nearly vanish, whereas in lower-capacity regions such as South Asia, a comparable enhancement in cooling appears (Fig. 5e). Similarly, the irrigation-induced increase in moist-heat extremes persists and intensifies in West Central Asia, whereas it weakens in certain grid cells over East Asia (Fig. 5f).

Rapid increase in exposure to heat extremes caused by climate change

We compare the mean annual exposure hours of extreme dry- and moist-heat events between the periods 2045–2074 and 1985–2014 to quantify changes caused by all forcings (e.g., greenhouse gas emissions change, land-use change, AEI change, and ITS change) under two scenarios. Relative to changes induced solely by irrigation, the combined forcings significantly increase the annual exposure to dry-heat extremes, particularly under SSP3-7.0 (Fig. 6a, c). For instance, in some grid cells across the Arabian Peninsula and East Africa, annual dry-heat extreme hours increase by ≥200 hours (i.e., ≥2.2 times more likely) under SSP1-2.6 and by ≥400 hours (i.e., ≥4.5 times more likely) under SSP3-7.0, despite the slight reduction in local dry-heat hours due to irrigation changes (Fig. 5c, e, g). Even in South Asia, where irrigation-induced cooling is most pronounced, dry-heat exposure still increases by more than 50 and 100 hours under the two scenarios, respectively. These findings suggest that, despite increased cooling impacts, irrigation alone cannot counteract the warming trend driven by other forcings. The spatial distribution of differences between the scenarios is relatively uniform, ranging from 50 to 400 hours across the majority of grid cells (Fig. 6e).

The increase in moist-heat extremes driven by all forcings is more substantial than that observed for dry-heat extremes, particularly in low-latitude regions (Fig. 6b, d). For instance, in Central Africa, the annual exposure to moist-heat extremes increases by more than 800 hours (≥8.8 times more likely) under SSP1-2.6, and exceeds 1600 hours (≥17.8 times more likely) under SSP3-7.0. In contrast, in mid- and high-latitude regions such as western North America, this increase is limited to ≤400 and ≤800 hours, respectively. The spatial pattern of scenario differences is similar, with increases exceeding 800 hours in tropical regions and ranging from 50 to 200 hours across most grid cells at higher latitudes (Fig. 6f). The contribution of irrigation to moist-heat exposure is relatively modest, with a few notable exceptions in arid regions characterised by high IWW. For example, in West Central Asia, where total forcings lead to increases of 200–400 hours and 400–800 hours, irrigation alone contributes an increase of ≥100 hours in certain grid cells under both scenarios (Fig. 5f, h). The results suggest that, under equivalent changes in greenhouse gas emissions and land use, the frequency of moist-heat events is more substantially impacted than that of dry-heat events. Consequently, great emphasis should be placed on assessing and mitigating the risks associated with moist-heat exposure in a warmer world.

Difference between dry- and moist-heat extremes hours increase enlarged by irrigation

Given the strong correlation between AEI and population density 5, we analyse the annual average hours of dry- and moist-heat extremes over intensively irrigated regions as a proxy for human exposure to extreme events. To this end, we consider two land categories, distinct from the three groups previously introduced, namely traditional irrigation hotspot areas, defined as grid cells where more than 20% of the area was irrigated during the historical period, and new irrigation hotspot areas, defined as grid cells where irrigation coverage was below 20% historically but exceeds 20% by the year 2070 (Fig. S7). Both types of extreme events become more frequent in the future across both scenarios, with moist-heat exposure hours increasing more rapidly than those for dry-heat, as indicated by the spatial patterns (Fig. 7). For instance, under SSP3-7.0, mean dry-heat exposure in traditional hotspot areas rises from under 100 hours during the historical period to approximately 250 hours in the 2070s, while moist-heat exposure increases from 300 hours to around 1400 hours (Fig. 7b, d). The divergence between scenarios becomes apparent after 2035, beyond which the rate of increase in exposure slows under SSP1-2.6.

Over traditional hotspot areas, irrigation-induced cooling effects on dry-heat and concurrent warming effects on moist-heat contribute to an amplified divergence in exposure hours between these two types of extreme events. For instance, under SSP3-7.0, annual exposure to dry-heat extremes is reduced by 50–100 hours, while exposure to moist-heat extremes increases by 20–100 hours (Fig. 7b). A comparable pattern is evident over new irrigation hotspot areas, where the difference in exposure between the two extremes can also widen by up to 100 hours (Fig. 7d). These findings underscore again the importance of monitoring local moist-heat metrics, as increases in moist-heat exposure do not follow a simple linear relationship with changes in dry-heat exposure. Furthermore, over intensely irrigated areas, irrigation plays an important role as an anthropogenic climate forcing, which should not be neglected in future studies.

Methods
Projected irrigation techniques share

In this study, we develop a new dataset of Irrigation Techniques Share (ITS) by employing a hydro-economic (HE) conceptual framework 30, which classifies countries based on two variables representing economic and hydrological conditions to project the future ITS under different scenarios. A central assumption of this framework is that countries with high socio-economic conditions and severe hydro-climatic conditions will invest more in high-efficiency irrigation. To verify this assumption and choose the variables representing these conditions, we calculate Spearman’s correlation coefficient between the historical ITS (drip, sprinkler, and flood) at national levels 27 and several variables from the Shared Socioeconomic Pathways (SSPs) datasets 51 and simulation outputs from the Inter-Sectoral Impact Model Intercomparison Project phase 3 (ISIMIP3) 52.

The socio-institutional capacity and hydro-climatic complexity of each country or region derived from those datasets are used to determine future change speed in irrigation system efficiency. SSP narratives were developed to characterize the societal future considering the challenges to mitigation and adaptation 33, independent of greenhouse gas emission scenarios. The SSP variables we use in this study include Gross Domestic Product per capita (GDP) 53, governance strength (GOV) 54, urbanisation (URB) 55, and the Gender Inequality Index (GII) 56. These datasets each cover more than 170 countries, at a 5-year frequency, from 1985 till 2100. Given the data availability of the hydro-climatic data, we chose SSP1 (the green road: low in both adaptation and mitigation challenges) and SSP3 (the rocky road: high in both challenges) to generate the data.

ISIMIP3 provides a framework under which researchers simulate the responses in different sectors to climate and socioeconomic changes in both historical periods (1850–2014) and future periods (2015–2100) under three SSP-RCP scenarios (SSP1-2.6 and SSP3-7.0), where RCP stands for Representative Concentration Pathway, indicating the level of future greenhouse gas concentrations. In the global water sector, three Global Hydrological Models (GHMs: CWatM 57, H08 58, and WaterGAP 2-2e 59) were forced by meteorological variables from five Earth system models (ESMs): GFDL-ESM4 60, IPSL-CM6A-LR 61, MPI-ESM1-2-HR 62, MRI-ESM2-0 63, and UKESM1-0-LL 64, to simulate the global water cycle at a spatial resolution of 0.5 by 0.5 degrees. We consider three variables: precipitation (P), precipitation divided by potential evaporation (PPET), and terrestrial water storage (TWS), either from the input or output datasets. To represent long-term hydro-climatic conditions, we calculate the 20-year average value (e.g., average value during 1986–2005 to represent the condition of the year 2005), and then average the values of all grid cells per country.

As 2010 is the last year before the projections in SSP dataset, we assume that the data of historical ITS represent the values in the year 2010, although the dataset is from multiple sources across a long period. Thus, we select the socioeconomic data in 2010 and the hydro-climatic data during the period 1996–2010 to perform the calculations.

Based on the correlation results (Table S2), we select GDP, GOV, URB and GII to represent socio-institutional capacity and P to represent hydro-climatic complexity. Given that the four socioeconomic variables have high correlations, we conduct a Principal Component Analysis for them and the resulting first four principal components explain 78.2%, 12.0%, 6.4%, and 3.4% of the variance, respectively. In the following analysis, we select the first principal component to represent socio-economic capacities. A high Spearman’s rank coefficient indicates a high correlation between two variables, so we assume that a pathway with higher socio-economic capacities tends to have higher motivation to improve irrigation efficiency. Similarly, P also shows a high, though less strong and significant, correlation with historical ITS, and we choose P as the variable representing hydro-climatic conditions (Table S2).

Based on limited and scarce national timeseries irrigation techniques fraction data from the USA 65 and Iran 66 (Table S3), we assign a default irrigation system update at 1% per year (that is, 1% reduction in the fraction of flood irrigation and 1% increase in the fraction of drip plus sprinkler irrigation). Flood irrigation is replaced by drip and sprinkler irrigation based on the fraction of drip and sprinkler crop functionality types (CFTs) (Table S4) in that grid cell. For example, if there are 30% drip CFTs, 50% sprinkler CFTs and 20% flood CFTs in one pixel, then a 1% decrease in flood irrigation would cause a 0.375% (i.e, 30/(30+50)) increase in drip irrigation and another 0.625% (i.e., 50/(30+50)) increase in sprinkler irrigation by default. The fraction is also limited by the suitability between irrigation techniques of CFTs, because drip irrigation can only be applied over drip CFTs, and sprinkler irrigation can only be applied over drip and sprinkler CFTs (Table S4, as defined before ref. 19). In other words, the maximum drip irrigation fraction is the fraction of drip CFTs, the maximum sprinkler irrigation is the fraction of drip CFTs plus sprinkler CFTs, and the minimum flood irrigation fraction is the fraction of flood CFTs. We normalise both variables based on the historical data (2010 for the socioeconomic variable and 1996–2010 for the hydroclimatic variable), then classify all countries based on their values. Since the socioeconomic variables consistently have a higher correlation with the ITS relative to the hydro-climatic variables (Table S2), we assign the socioeconomic principal component variable a higher importance (see Table S5). The rate of change is therefore adjusted every five years based on socio-economic and 20-year mean hydrologic conditions.

Given the complexity of the process, a representative example is presented for illustrative purposes (a flowchart is also given as shown in Fig. S8). In the year 2075, a grid cell is assumed to contain 70% flood irrigation, 20% sprinkler irrigation, and 10% drip irrigation. The crop type distribution is assumed to be 35% flood crops, 50% sprinkler crops, and 15% drip crops (see Table S4). The procedure begins by calculating the normalised 20-year average precipitation for the period 2056-2075, which is assumed to be 0.45. Subsequently, the first principal component of the socio-economic indicators–GDP, GOV, URB, and GII–is computed using the PCA model previously developed from historical SSP datasets, yielding a normalised value of 1.25. The annual updating rate is determined by subtracting the hydro-climatic adjusting factor (0.1) from 1 and adding the socio-economic adjusting factor (0.8) (Table S4), resulting in a rate of 1.7% per year over the subsequent five-year period (until 2080) (Table S5). Accordingly, by 2080, the flood irrigation fraction is reduced by 8.5% (1.7% × 5 years), with the reduction redistributed to sprinkler and drip irrigation in proportions of 6.54% and 1.96%, respectively. Thus, by 2080, the shares of flood, sprinkler, and drip irrigation are adjusted to 61.5%, 26.54%, and 11.96%, respectively.

CESM2 with the new irrigation module and simulations

The Community Earth System Model version 2 (CESM2) is an open-source Earth system model that has been widely used to detect irrigation-induced impacts 5,11,67. The irrigation module in CESM2 has been expanded to represent four different irrigation techniques: drip, sprinkler, flood, and paddy (used by default if the crop type is irrigated rice) 19. These irrigation techniques vary in the activation condition, soil water threshold, and target, where the water is applied, and whether surface water is ponded, showing satisfying results for both global and single-point simulations 19. However, currently, the cropland land unit is divided into 64 columns, one rainfed and one irrigated for all 32 CFTs, which mean that only one irrigation method is assigned to each separate CFT per grid cell. To allow three different irrigation techniques for each CFT per grid cell, we expand the cropland unit to 128 columns: one rainfed, one drip irrigated, one sprinkler irrigated, and one flood irrigated for each CFT. The calculations of the land fluxes are conducted individually over each column before being averaged to the grid cell level. Transient ITS is implemented through the preparation of input land use and management timeseries, in which we provide the annual information of ITS from the year 2015 to 2100. In this study, we do not limit the water withdrawal for irrigation based on river flow, and if river flow cannot meet the water requirement of irrigation, the deficit is extracted from the ocean to close the water balance.

After the model source code development, we create the land use data with irrigation techniques incorporated, based on the land use of the year 2010. In a previous study 27, a static grid cell-scale ITS dataset was developed for the historical period (sources ranging from 1990 to 2010), which is used as the starting point for data generation. The general process is firstly assigning drip irrigation to drip crops (as defined before ref. 19), as drip irrigation is only suitable for drip crops (see Table S4). Then, sprinkler irrigation is assigned to all sprinkler crops, and in case all sprinkler crops are irrigated by the sprinkler technique, the extra sprinkler irrigation fraction is used for drip crops. After these two processes, flood irrigation is given to all irrigated cropland left. Then, to build the land use timeseries from 2015 to 2074 under SSP1-2.6 and SSP3-7.0, we performed a linear interpolation to transfer 5-year irrigation technique share data to annual data, then distribute the change in irrigation techniques share to different CFTs as mentioned.

We first conduct the two historical simulations with (Hist_IRR) and without irrigation (Hist_NOI) covering the period 1980-2014, with first five years as the spin-up period, using the land use and land management of the year 2010, and including the existing 27 ITS dataset (Fig. 8). Each simulation contain three ensemble members with a very small difference in the initial atmospheric conditions 11. Then, we generate four ensembles for the future period (2015–2074), containing three members each and branching from the corresponding HIST simulations (Fig. 8). The future scenarios ensembles encompass two SSP-RCPs (SSP1-2.6 and SSP3-RCP7.0:) and for each emission scenario we generate an ensemble with (IRR) and without (NOI) transient irrigation extent and technique shares (SSP1_IRR, SSP1_NOI, SSP3_IRR, and SSP3_NOI). All simulations employ a horizontal resolution of 0.9° x 1.25°. In the historical simulation, the land use and irrigation technique share are fixed at the present-day level, and other climate forcings (e.g., greenhouse gas emissions) are transient, while in the future simulations, both the climate and land use forcings are transient as well. In CESM2, when transient land-use is activated, the surface map is updated yearly, and the ITS is incorporated into the surface map, which enables the model to use transient ITS. During the simulation, the heat metrics module 68 is switched on, which mean that all heat metrics are calculated regularly at a 3-hourly frequency. We focus on only Tw, as most other metrics are a weighted average of Tw and T2m. In this module, a simplified calculation method of Tw is applied, as shown in Equation (1):

Tw = T2m arctan(0.151977 \phi + 8.313659) + arctan(T2m + \phi) - arctan(\phi - 1.676331) + 0.00391838 \phi 3/2 arctan(0.023101 \phi) - 4.68035 (1)

where T2m is the 2-metre air temperature in degrees Celsius, and \phi indicates 2-metre air relative humidity in %.

CESM2 output analysis

Heat extreme indices at 3-hourly temporal resolution are obtained from the outputs of simulations, including T2m and Tw, which are calculated as described in the module 68. We compare the multi-year average number of hours per year with extreme dry or humid heat conditions between different combinations of experiments (IRR and NOI) and periods (historical: 1985-2014 and future: 2045–2074) to separate the impacts of all forcings under each SSP-RCP scenario (changes in climate, land use, and irrigation), all forcings between two SSP-RCP scenarios, and irrigation (Fig. 8). To assess the temporal evolution of irrigation-induced impacts, we also calculate annual frequency of dry- and moist-heat extremes (in hours), on average over traditional hotspot areas and new hotspot areas, which are defined as grid cells where more than 20% of their area is irrigated in the year 2010, and grid cells where less than 20% of their area is irrigated in the year 2010 but AEI increases by more than 20% until the year 2070, respectively (Fig. S7).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Review of “Compounding future escalation of emissions- and irrigation-induced increases in humid-heat stress” submitted to\nNature Communications for consideration for publication.\nThe manuscript describes a global study on the impact of climate change on irrigation development under 2 SSPs. The\nauthors developed a new irrigation module for CESM2, which now considers more irrigation techniques. The model is then\napplied under a historical scenario and for different SSPs in the 21st century. The results show that irrigation demand will\ndecrease under SSP1 and increase under SSP3. Based on the results of the simulations, the authors also determined the\nimpacts on dry-heat and moist-heat stress. These results show moderate changes for dry-heat stress, but substantial\nincreases for moist-heat stress under the different SSPs. The study seems a valuable addition to the literature, with novel\nresults related to the projection of irrigation for the 21st century, including the impacts on dry-heat and moist-heat stress.\nThere are two issues regarding the considered SSPs that would need some more explanation in the manuscript. The\nauthors mainly focus on two SSPs, i.e. SSP1 and SSP3, while SSP5 is also considered, but not fully included in the results.\nThis is somewhat confusing. Please provide the reasoning why SSP5 is not fully considered. In either case, it would make\nsense to either exclude SSP5 from the manuscript all together or better integrate the SSP5 results in the manuscript.\nIn addition, I was not expecting that SSP3 would result in a such a substantial increase in irrigation. Following O’Neill et al.\n(2017), it seems that SSP5 is a more likely pathway that would result in such an increase in irrigation, given the assumption\nthat agriculture will be “Highly managed, resource-intensive; rapid increase in productivity” under SSP5. Agriculture under\nSSP3 is suggested to be characterized by “Low technology development, restricted trade”, which does not suggest such a\nhigh focus on irrigation, especially drip irrigation. The description of how the different SSPs affect irrigation development fall\nshort in this respect (lines 573-586). Hence, the authors should better explain how the different SSPs are implemented and\ndiscuss in the main document how the implementation of the SSPs compare to the literature, e.g. O’Neill et al. (2017).\nO’Neill, B.C., Kriegler, E., Ebi, K.L., Kemp-Benedict, E., Riahi, K., Rothman, D.S., van Ruijven, B.J., van Vuuren, D.P.,\nBirkmann, J., Kok, K., Levy, M., Solecki, W., 2017. The roads ahead: Narratives for shared socioeconomic pathways\ndescribing world futures in the 21st century. Global Environmental Change 42, 169–180.\nhttps://doi.org/10.1016/j.gloenvcha.2015.01.004\nConsidering the results, the authors show the results for dry-heat and moist-heat stress for scenarios with and without\nirrigation. I am not fully convinced of the added value of showing the results without irrigation, at least in the main document.\nThe first part of the Results section solely focuses on how irrigation is projected to change under the different SSPs. In the\nsecond part the authors also consider scenarios without irrigation. Although these scenarios can show how heat stress is\naffected by climate change only, they are not realistic scenarios, but more “what if” scenarios. I suggest to move the\nscenarios without irrigation to the Supporting Material and mostly focus on the scenarios with irrigation, also when\npresenting the results in the text. This would significantly improve the readability of the text, where especially lines 176-222\nare sometimes difficult to follow because of the number of different comparisons that are made by the authors, i.e. historical\nvs SSP and irrigation vs no irrigation between the SSPs and between historical and the SSPs. In that sense, for Figures 4\nand 5, I would keep panels a, c and d in the main document, while b, e and f can be moved to the Supporting Material.\nThe concluding remarks focus mostly on the suggestion to move to an SSP1 world (lines 321-330 and lines 341-353). From\nthe perspective of sustainability, the goal to decrease irrigation water use and the impacts on heat stress, I fully agree with\nthis. But is this also a realistic goal? Since the authors considered only two SSPs, which are in most aspects also their\nopposites, it seems logic that the authors come to the conclusion that SSP1 would be better aim. However, it is likely that\nother SSPs may be more realistic given the ongoing socioeconomic and climatic developments. More discussion is needed\nthat would need the involvement of the other SSPs, given their particularities and possible impacts on irrigation and heat\nstress.\nBelow I have provided specific comments to the text, figures and tables.\nSpecific comments\nLines 31-32: With “…its climatic impacts in future with possible changes in both extent and efficiency considered” the\nauthors mean “… its future climatic impacts considering possible changes in both extent and efficiency”.\nLine 42: Replace “increase” with “increases”.\nLine 43: The authors mean ≥100 hours yr-1? Please revise.\nLines 93-94: Why is it relevant to mention that SSP5-8.5 is also simulated but not considered? Please clarify in the text.\nLines 96-98: The text between the parentheses depends on the socioeconomic scenario, right? Please clarify this in the text.\nLines 110-113: So, the irrigated area is based on LUH2? This has not been mentioned in the Methods section. I suggest to\nrevise this sentence and start the sentence with a result from this study, instead of with the projections from LUH2.\nFigure 1: Please revise the figure caption, where it says two times “equipped”, while one time seems sufficient.\nFigure 2d-2g: It is difficult to understand where the historical IWW results are shown. I suggest to make the grey bar as wide\nas the historical period (1985-2014), to make this stand out more. Moreover, I don’t see the point of the vertical green and red\nlines for the two SSP scenarios. I suggest to remove these lines and only include the trend line.\nLines 131-151: The results of Figure 2 are presented here, going progressively from panel a to g. I suggest to integrate better\nthe results of panels a-c and panels d-g. For instance, by presenting first the results of SSP1 and discussing simultaneously\nthe results of panels d-g for this scenario, etc.\nLines 282-288: I would say that farmers’ individual decision-making and government regulation are part of the\nsocioeconomic conditions. But I can be wrong.\nLines 270-305: Limitations are usually discussed at the end of the Discussion section.\nLines 583-586: Please explain here why the authors focused on these two extreme SSPs, i.e. SSP1 and SSP3 (not\nconsidering SSP5). Moreover, in the main text the authors focus on SSP1 and SSP3. It is a bit confusing that results are\nshown for SSP5 also, but not really considered in the main document. I suggest to remove this from the manuscript or\nincorporate the results in the main text.\nLines 620-632: I suggest to include a flow chart to make this easier to understand.\nLine 671: Please give the definition of NOI.\n(Remarks on code availability)\nA README file is provided, but only gives limited information on what the different scripts accomplish.\nIn general, most scripts do not include comments on the functionality of the script. It is therefore difficult to understand what\nthe code is meant for. Moreover, the script are not particulary DRY (Don't Repeat Yourself), there is a lot of repitition, which\nmakes the code difficult to understand. Also the code is written in three different programming languages (Matlab, Python\nand shell script), which is of course not ideal.", "reviewer_2": "General Comments:\nThis is an interesting article that investigates the irrigation impacts on extreme dry and humid heat under future scenarios of\nchanges in irrigation extent and irrigation efficiency. The authors set up different SSP-RCP pathway scenarios to develop\ndynamic global irrigation techniques share (ITS) datasets and embed them in the CESM2 Model, and conduct sensitive\nsimulation experiments with CESM2 to assess irrigation water withdraw and quantify irrigation impacts on extreme dry and\nhumid heat under dynamic conditions of changing irrigation extent and efficiency. The innovation of this study is the\ndevelopment of the dataset that considers irrigation technology change using a combination of SSP and ISIMIP3 scenarios\nembedded in the CESM2 for annual updates of the global ITS. In the context of increasing global heat stress, this research\ntopic has received much attention recently, and this study highlights the importance of sustainable socio-economic pathways\nand efficient irrigation technologies to provide more solid evidence on those issues. The manuscript is clearly written, the\nresults are well presented through several high-quality tables and graphs, overall I would recommend the manuscript for\npublication in Nature Communications but have a few comments that might be considered in a revised version of the\nmanuscript:\nMajor comments:\n1-Current study was conducted based on a combination of SSP1, SSP3 with RCP2.6, 7.0 scenarios, and the fact that the\nfindings are very sensitive to the choice of SSP-RCP scenarios, a discussion of the uncertainty in the results due to the\nchoice of different pathways seems to be necessary.\n2-I agree with the key hypothesis that “richer and drier countries will invest more in high-efficient irrigation”. For the East Asia\nregion, with the irrigated area remaining essentially unchanged, IWW has been reduced by about 30-40% through\nimprovements in irrigation water use efficiency as irrigation technology has evolved (estimated from Figure 2). In East Asia,\nChina has the most potential and motivation to improve irrigation efficiency. In the case of China, for example, the current\nirrigation water use efficiency is about 0.56, and the results in the manuscript suggest that the water use efficiency would\nneed to be raised to nearly 1 in the future, which confuses me. Consideration of irrigation efficiency threshold constraints in\nthe ITS dataset seems necessary, if possible.\n3-In this study, it is assumed that the evolution of drip and sprinkler irrigation to replace flood irrigation is at a constant rate of\n1%, and as you mentioned, paddy irrigation is included in flood irrigation, and planting rice with drip and sprinkler irrigation\ntechniques seems technically challenging currently. Especially in East Asia, where rice cultivation is dominant, will this\nunderestimate the irrigation impacts on extreme dry and humid heat in future scenarios?\n4-CESM2 has no limits on irrigation withdrawals for the irrigation module, and in fact irrigation withdrawals are limited by the\nregional river flows, and for the future new hotspots mentioned in the manuscript, whether the unconstrained application of\nirrigation withdrawals would overestimate the irrigation impacts on the extreme heat in the region represented by the Middle\nEast and Africa.\n5-Lines 219-222 illustrate that irrigation practices in traditionally highly irrigated areas have minor impacts on extreme dry\nheat under the future scenarios, this point deserves more in-depth comparative analysis and discussion. The comparisons in\nFigures 4c and f seem to compare extreme dry heat differences between different SSP-RCP pathways rather than the impact\nof irrigation.\n6-As you mentioned in line 315, some areas that are currently considered unsustainable for irrigation practices continue to\nexperience an increase in irrigation water withdrawal due to irrigation expansion under future scenarios, and their viability\ndeserves further validation and discussion.\n7-This study is a continuation of the authors' recent achievement (DOI:10.1038/s41467-025-56356-1), which has in-depth\ninsights in this field, and I believe that if they could consider how to guide the actual irrigation patterns towards the most\nbeneficial direction for human based on the previous quantitative analysis and mechanism exploration studies of irrigation\nimpacts on heat stress in the discussion section, it will provide a solid theoretical basis for the climate change adaptation\nstrategies in agriculture.\nMinor comments:\n1-The abbreviation of Figure 4 on comparing the differences between future and historical scenarios fails to convey a clear\nenough meaning.\n2-Provide formulas for key evaluation indicators, such as wet bulb temperature, in the methodology section for better\nindependent understanding.\n3-The figures and tables in the Appendix would be better as Support Information for the manuscript.\n4-Driving CESM2 simulations using SSP-RCP scenarios and innovative ITS datasets is the core of this study, and the\nnecessary descriptions of the CESM2 modeling related setup in the Methods section (or support information) will make the\npaper more perfect.\nLiangfeng Zou (PhD student) and Yuanyuan Zha (Associate Professor) at Wuhan University\n(Remarks on code availability)\nthe code is properly provided.", "reviewer_3": "The study “Compounding future escalation of emissions- and irrigation-induced increases in humid-heat stress” discusses\ndifferent irrigation scenarios and their impact on various heat events. It is a valuable contribution to understanding the\nfeedback between irrigation methods and local heat stress. I thoroughly enjoyed reading the paper. I have a few comments\nthat may help expand the discussion, which are listed below:\n1) What if the assumed ITS transition rate of 1% per year is not accurate? Can you briefly describe what would happen if the\nITS rate were different, and what the plausible scenarios might be?\n2) Doubling the number of cropland columns from 64 to 128 increases model complexity and computational cost. If the data\nused to allocate irrigation methods across columns is uncertain or based on assumptions, how do you ensure that this\nadded complexity results in more reliable outcomes? Can you also discuss the limitations of the model used?\n3) Could you elaborate on the assumptions underlying the hydro-economic framework? For instance:\ni) Classifying countries by national-level variables misses within-country variability in both irrigation practices and driving\nfactors\nii) Since the driver selection is based on Spearman’s correlation, how do you address the fact that correlation does not imply\ncausation?\niii) Why is precipitation used as the sole indicator of hydro-climatic conditions, and how do you account for other key factors\nlike groundwater availability, river discharge, infrastructure, or water rights?\niv) The framework is static and scenario-based: it projects future ITS based on fixed drivers but does not simulate feedbacks\n(e.g., how changes in ITS might influence GDP, water stress, or governance over time).\n(Remarks on code availability)"}, "author_response": "Contents\n1.1 Majorcomments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4\nReviewer1Comment1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4\nReviewer1Comment2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4\n1\nReviewer1Comment3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5\nReviewer1Comment4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6\nReviewer1Comment5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8\n1.2 Minorcomments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9\nReviewer1Comment6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9\nReviewer1Comment7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9\nReviewer1Comment8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9\nReviewer1Comment9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9\nReviewer1Comment10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9\nReviewer1Comment11 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11\nReviewer1Comment12 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13\nReviewer1Comment13 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13\nReviewer1Comment14 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13\nReviewer1Comment15 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13\nReviewer1Comment16 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14\n1.3 Remarksoncodeavailability . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15\nReviewer1Comment18 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15\n2.1 Majorcomments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16\nReviewer2Comment1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16\nReviewer2Comment2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16\nReviewer2Comment3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17\nReviewer2Comment4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19\nReviewer2Comment5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20\nReviewer2Comment6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20\nReviewer2Comment7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20\nReviewer2Comment8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21\n2.2 Minorcomments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22\nReviewer2Comment9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22\nReviewer2Comment10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22\nReviewer2Comment11 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22\nReviewer2Comment12 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22\n3.1 Majorcomments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23\nReviewer3Comment1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23\nReviewer3Comment2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23\nReviewer3Comment3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23\nReviewer3Comment4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25\nCONTENTS 3\nAbstract\nThisresponselettercontainsnumberedfiguresandtablesandreferencestothem. To\npreventconfusion,thefiguresandtablesembeddedwithinthisresponseletterarecalled\nFIGs and TABs, while the figures and tables embedded within the manuscript are still\ncalledFiguresandTables. Thefollowingconventionisappliedtodenotemodificationin\ntheoriginalmanuscript: newtext.\n1.1 Majorcomments\nReviewer1Comment1\nThe manuscript describes a global study on the impact of climate change on irriga-\ntion development under 2 SSPs. The authors developed a new irrigation module for\nCESM2, whichnowconsidersmoreirrigationtechniques. Themodelisthenapplied\nunderahistoricalscenarioandfordifferentSSPsinthe21stcentury. Theresultsshow\nthat irrigation demand will decrease under SSP1 and increase under SSP3. Based on\nthe results of the simulations, the authors also determined the impacts on dry-heat\nand moist-heat stress. These results show moderate changes for dry-heat stress, but\nsubstantial increases for moist-heat stress under the different SSPs. The study seems\na valuable addition to the literature, with novel results related to the projection of ir-\nrigationforthe21stcentury,includingtheimpactsondry-heatandmoist-heatstress.\nResponseWethankReviewer1fortheseencouragingcommentsandthevaluablefeedback,\nwhich has helped us to improve our manuscript. Below, we address every comment care-\nfullyandexplainthecorrespondingchangesinthemanuscript.\nReviewer1Comment2\nTherearetwoissuesregardingtheconsideredSSPsthatwouldneedsomemoreexpla-\nnationinthemanuscript. TheauthorsmainlyfocusontwoSSPs, i.e. SSP1andSSP3,\nwhileSSP5isalsoconsidered,butnotfullyincludedintheresults. Thisissomewhat\nconfusing. Please provide the reasoning why SSP5 is not fully considered. In either\ncase, it would make sense to either exclude SSP5 from the manuscript all together or\nbetterintegratetheSSP5resultsinthemanuscript.\nResponse Thanks for pointing this out. We are sorry for the confusion it creates. In this\nstudy, the irrigation techniques share data was generated under all three scenarios (SSP1-\n2.6, SSP3-7.0 and SSP5-8.5), because we have access to the SSP and ISIMIP datasets under\nthese three scenarios. Regarding the simulations, as we were limited by computational re-\nsources, we selected the two most extreme scenarios (SSP1-2.6: both low challenges; and\nSSP3-7.0: both high challenges) (1) out of three to proceed with. To avoid the confusion,\nwe fully exclude SSP5 from the main text and move it to the Supplementary Materials (see\nSupplementary Figures 2.2). Some texts are also added to let readers know that the dataset\nunderSSP5-8.5hasalsobeencreated.\nL93-96\nTwo SSP-RCPs, SSP1-2.6 (the green road, with low challenges for both miti-\ngationandadaptation)andSSP3-7.0(therockyroad,withbothhighchallenges),\nare selected (the dataset under SSP5-8.5 is also generated, see Supplementary\nInformation,butisnotusedfurtherinthesimulations).\nL624-628\n1. Reviewer1 5\nGiven the data availability of the hydro-climatic data, we choose SSP1 (the\ngreen road: low in both adaptation and mitigation challenges), SSP3 (the rocky\nroad: high in both challenges), and SSP5 (the highway: high in challenges for\nmitigation and low in challenges for adaptation) to generate the data (SSP5 not\nusedinsimulations).\nReviewer1Comment3\nIn addition, I was not expecting that SSP3 would result in a such a substantial in-\ncreaseinirrigation. FollowingO’Neilletal. (2017),itseemsthatSSP5isamorelikely\npathwaythatwouldresultinsuchanincreaseinirrigation,giventheassumptionthat\nagriculturewillbe“Highlymanaged,resource-intensive;rapidincreaseinproductiv-\nity” under SSP5. Agriculture under SSP3 is suggested to be characterized by “Low\ntechnology development, restricted trade”, which does not suggest such a high fo-\ncusonirrigation,especiallydripirrigation. ThedescriptionofhowthedifferentSSPs\naffect irrigation development fall short in this respect (lines 573-586). Hence, the au-\nthors should better explain how the different SSPs are implemented and discuss in\nthe main document how the implementation of the SSPs compare to the literature,\ne.g. O’Neill et al. (2017). O’Neill, B.C., Kriegler, E., Ebi, K.L., Kemp-Benedict, E.,\nRiahi, K., Rothman, D.S., van Ruijven, B.J., van Vuuren, D.P., Birkmann, J., Kok, K.,\nLevy, M., Solecki, W., 2017. The roads ahead: Narratives for shared socioeconomic\npathwaysdescribingworldfuturesinthe21stcentury. GlobalEnvironmentalChange\n42,169–180. https://doi.org/10.1016/j.gloenvcha.2015.01.004\nResponse We thank Reviewer 1 for this constructive suggestion of expanding the descrip-\ntionoftheimpactsofdifferentSSPsonirrigationdevelopment. Weagreethatintheprevious\nversion,thedescriptionwasnotcomprehensive. Inthisstudy,theupgraderateofirrigation\nequipmentistotallydependentonthesocio-economiccapacitiesandhydro-climaticcondi-\ntions.\nThevariablesrepresentingsocio-economiccapacitiesareobtainedfromtheSSPdataset,and\ntheoutcomesalignwiththenarrativesofSSP.InTAB1wecanfindtheassumptionsregard-\ninglanduseandagriculture.\nTAB1: Assumptionsregardingland-useandagricultureofSSPnarratives(1;2)\nSSPs Landuse Agriculture Irrigation\nSSP1 Strong regulations to avoid Improvements in ag produc- Large improvements in\nenvironmentaltradeoffs tivity; rapid diffusion of best irrigation water use ef-\npractices ficiencywherepossible\nSSP3 Hardly any regulation; con- Low technology develop- Only modest improve-\ntinued deforestation due to ment,restrictedtrade ments in irrigation wa-\ncompetition over land and teruseefficiency\nrapid expansion of agricul-\nture\nSSP5 Medium regulations lead to Highly managed, resource- Large improvements in\nslowdeclineintherateofde- intensive; rapid increase in irrigation water use ef-\nforestation productivity ficiency\nSSP3representsarockyroadwithregionalrivalry,underwhichthegapbetweendeveloped\nand developing countries grows bigger. The population is expected to grow at a high rate\nin developing countries, increasing the global food demand. At the same time, the priority\nfor addressing environmental issues is low, so the irrigation is assumed to increase with a\nlow rate of efficiency increase. SSP5 represents a pathway with rapid economic and social\ndevelopment. Environmental issues are expected to be solved via technological solutions\nin the second half of the century. It is confusing that irrigation extent experiences a rapid\nincrease under SSP3, even though it is designed to be low-managed. The reason for the\nconfusion is that in our study, we do not see flood irrigation as a highly-managed activity.\nInstead,floodirrigationcouldbeachievedwithlittleinvestmentinequipment,anditshould\nbeseenasaveryinefficientwayofwater-use.\nWealsoaddedsometextintheDiscussionSection: L260-273\nThe assumptions regarding land use and agriculture under SSP1 and SSP3\nare provided as \"strong regulations to avoid environmental tradeoffs; improve-\nmentsinagricultureproductivity;rapiddiffusionofbestpractices\",and\"Hardly\nany regulation; continued deforestation due to competition over land and rapid\nexpansion of agriculture; Low technology development, restricted trade\". The\nLUH2 dataset agrees with those narratives, with the key feature that AEI keeps\nalmostconstantunderSSP1-2.6andexpandsrapidlyunderSSP3-7.0(Figure1,2).\nSome assumptions regarding the irrigation water use efficiency have also been\nproposed, with \"large improvements in irrigation water use efficiency where\npossible\" for SSP1 and \"only modest improvements in irrigation water use ef-\nficiency\" for SSP3. The generated ITS data align well with those assumptions,\nwitharapiddecreaseintraditionalinefficientfloodirrigationunderSSP1-2.6and\na slower decrease under SSP3-7.0 (Figure 1, 3). Although we apply a simplified\nadjustment factor to account for annual ITS changes based on socio-economic\nand hydro-climatic variables (see Methods), the resulting estimates are consis-\ntent with prevailing assumptions, thereby offering a valuable enhancement to\nexistingdatasets.\n1. Reviewer1 7\nReviewer1Comment4\nConsidering the results, the authors show the results for dry-heat and moist-heat\nstressforscenarioswithandwithoutirrigation. Iamnotfullyconvincedoftheadded\nvalue of showing the results without irrigation, at least in the main document. The\nfirstpartoftheResultssectionsolelyfocusesonhowirrigationisprojectedtochange\\underthedifferentSSPs. Inthesecondparttheauthorsalsoconsiderscenarioswith-\nout irrigation. Although these scenarios can show how heat stress is affected by cli-\nmatechangeonly,theyarenotrealisticscenarios,butmore“whatif”scenarios. Isug-\ngest to move the scenarios without irrigation to the Supporting Material and mostly\nfocusonthescenarioswithirrigation,alsowhenpresentingtheresultsinthetext. This\nwouldsignificantlyimprovethereadabilityofthetext,whereespeciallylines176-222\naresometimesdifficulttofollowbecauseofthenumberofdifferentcomparisonsthat\naremadebytheauthors,i.e. historicalvsSSPandirrigationvsnoirrigationbetween\nthe SSPs and between historical and the SSPs. In that sense, for Figures 4 and 5, I\nwouldkeeppanelsa,canddinthemaindocument,whileb,eandfcanbemovedto\ntheSupportingMaterial.\nResponse\nWe thank Reviewer 1 for this suggestion. We changed the structure of the Results section\nto discuss the impacts of climate change and irrigation separately, which can help avoid\nconfusion. WealsomovedallresultswithoutirrigationtotheSupplementaryMaterials. In\ntheoldversionofthemanuscript,westructuredtheResultssectionasfollows.\n1. Irrigationwaterwithdrawalhighlydependonprojectedirrigationexpansionandeffi-\nciency\n2. Nomorereversalofdry-heatextremesinirrigationhotspots\n3. Intensifiedlocalmoist-heatstress\nAfter introducing the changes in the area equipped for irrigation and irrigation techniques\nshare, we described the changes in dry- and moist-heat extremes separately, in which we\ntalked about the impacts of different forcings, and focused on both spatial distribution and\ntimeseries,whichcouldbeconfusing.\nToaddressthisissue,inthenewversionofthemanuscript,westructuretheResultssection\nasfollows.\n1. Irrigationwaterwithdrawalhighlydependonprojectedirrigationexpansionandeffi-\nciency\n2. Divergentimpactsofirrigationondry-andmoist-heatextremes\n3. Rapidincreaseinexposuretoheatextremescausedbyclimatechange\n4. Difference between dry- and moist-heat extremes hours increase enlarged by irriga-\ntion\nWealsochangedFigure4,Figure5,andFigure6,whicharenowrespectivelythefiguresfor\nimpactsofirrigation,impactsofallforcings,andtimeseries.\nWe now first introduce the impacts of irrigation on moist-heat extremes (subsection 2), and\nthen describe the impacts of all forcings (subsection 3), and finally, we compared the im-\npartsofirrigationandotherforcings(subsection4). Wehopethenewversioncanavoidthe\nconfusion.\nReviewer1Comment5\nThe concluding remarks focus mostly on the suggestion to move to an SSP1 world\n(lines 321-330 and lines 341-353). From the perspective of sustainability, the goal to\ndecrease irrigation water use and the impacts on heat stress, I fully agree with this.\nBut is this also a realistic goal? Since the authors considered only two SSPs, which\nare in most aspects also their opposites, it seems logic that the authors come to the\nconclusion that SSP1 would be better aim. However, it is likely that other SSPs may\nbemorerealisticgiventheongoingsocioeconomicandclimaticdevelopments. More\ndiscussion is needed that would need the involvement of the other SSPs, given their\nparticularitiesandpossibleimpactsonirrigationandheatstress.\nResponse We fully agree with Review 1 on this point, so we changed some texts to stress\nthat the results of this study indicate the importance of increasing the irrigation efficiency\nand avoiding unsustainable irrigation expansion, rather than focusing on SSP1, which is a\nhighlyidealisedandoutdatedscenario.\nWehaveaddedsometextinthediscussionpart.\nL251-258\nIn this study, we, for the first time, design ITS scenarios aligned with the\nSSP-RCPs, improve an Earth system model to incorporate this information, and\nperform fully-coupled climate simulations under varying climate and irrigation\ntransitionpathways. Wethenanalysethesimulationoutputsforirrigationwater\ndemandandirrigation-inducedimpactsondryandmoist-heatstress. Theresults\nexhibitastrongsensitivitytothechoiceofSSP-RCPscenarios,highlightingtheir\nimportant implications for future irrigation planning and development. While\nthesescenariosrelyonseveralidealisedassumptions, theunderlyingnarratives\nstilloffervaluableinsightsforguidingfutureirrigationstrategies.\nL371-376\nGenerally, the results exhibit a strong dependence on the selected scenarios,\nwith this study focusing exclusively on SSP1 and SSP3. These scenarios rep-\nresent two contrasting extremes in terms of climate mitigation and adaptation\nrisks. However, they may no longer reflect the most current socioeconomic and\nclimate projections, as newer scenarios have since been developed to address\ncontemporaryresearchquestions. Futureirrigation-relatedstudiesshouldthere-\nforeincorporateupdatedscenarioframeworkstoyieldmorerelevantinsightsfor\npolicyandplanning.\n1. Reviewer1 9\n1.2 Minorcomments\nReviewer1Comment6\nLines 31-32: With “...its climatic impacts in future with possible changes in both ex-\ntentandefficiencyconsidered”theauthorsmean“... itsfutureclimaticimpactscon-\nsideringpossiblechangesinbothextentandefficiency”.\nResponse We thank Review 1 for the suggestion to rephrase this sentence. It has been\nchangedinthemanuscript.\nL30-32\nIrrigation has been investigated as an important historical climate forcing,\nbut there is no study exploring its future climatic impacts considering possible\nchangesinbothextentandefficiency.\nReviewer1Comment7\nLine43: Theauthorsmean≥100hoursyr-1? Pleaserevise.\nResponseThanksforfindingthemissingunit. Wehaveaddedtheunit.\nReviewer1Comment8\nLines 93-94: Why is it relevant to mention that SSP5-8.5 is also simulated but not\nconsidered? Pleaseclarifyinthetext.\nResponseWenowmoveallmaterialsregardingSSP5-8.5totheSupplementaryMaterialsto\navoidconfusion.\nReviewer1Comment9\nLines96-98: Thetextbetweentheparenthesesdependsonthesocioeconomicscenario,\nright? Pleaseclarifythisinthetext.\nResponseThanksforpointingitout. Wehaveaddedsometextthere.\nL98-100\nA basic assumption is that countries with higher socio-economic capacities\nand drier climate conditions have more motivation to upgrade their irrigation\nsystem,whichvaryamongscenarios.\nReviewer1Comment10\nFigure 1: Please revise the figure caption, where it says two times “equipped”, while\nonetimeseemssufficient.\nResponseThanksforthissuggestion. Wehavedeletedone\"equipped\"there.\nReviewer1Comment11\nFigure2d-2g: ItisdifficulttounderstandwherethehistoricalIWWresultsareshown.\nI suggest to make the grey bar as wide as the historical period (1985-2014), to make\nthisstandoutmore. Moreover,Idon’tseethepointoftheverticalgreenandredlines\nforthetwoSSPscenarios. Isuggesttoremovetheselinesandonlyincludethetrend\nline.\nResponse\nThanksforthisadvice! Wehavereplottedthefigure(seeFIG1.)\n1. Reviewer1 11\nFIG 1: Historical irrigation water withdrawal and projected changes under SSP1-2.6 and\nSSP3-7.0scenarios. aAnnualmeanirrigationwaterwithdrawal(IWW)inthehistoricalpe-\nriod (1985-2014). b,c Projected changes in annual mean IWW in future period (2045-2074)\nunder SSP1-2.6 (b) and SSP3-7.0 (c), compared to the historical period. The values shown\nherearethemeanvaluesofallthreeensemblemembers. d-gGlobal(d)andsumofregional\nannual IWW of three groups of regions (e-g) during the historical (1985-2014) and future\n(2015-2074) periods under SSP1-2.6 and SSP3-7.0. Historical IWW is visualised as a single\nbar in the year 2000, as in the historical simulations, AEI and ITS are static. The bar in-\ndicates the mean value and the range indicates the maximum and minimum values of the\n30-year period. In future periods darker bars indicate the range simulated by three ensem-\nblemembersandlinesindicatethemedianvalues. Allvaluesinthefutureperiodhavebeen\nsmoothedusingSavitzky-Golayfiltering(order=2,window=7). Regionsofeachgroupare\nlisted in Table S1, and their locations are visualised in Figure a. IWW of individual regions\narevisualisedinFigureS6.\nReviewer1Comment12\nLines 131-151: The results of Figure 2 are presented here, going progressively from\npanel a to g. I suggest to integrate better the results of panels a-c and panels d-g.\nForinstance,bypresentingfirsttheresultsofSSP1anddiscussingsimultaneouslythe\nresultsofpanelsd-gforthisscenario,etc.\nResponse Thanks for this suggestion! We have restructured this part of results. Now we\ndiscusstheresultsofSSP1andSSP3separately. Wehopeitcanavoidtheconfusion.\nL112-158\nThe Land Use Harmonization version 2 (LUH2) dataset shows that under\nSSP1-2.6, the global AEI remains nearly constant at around 2.7-2.8 × 106 km2,\nwith a rapid increase in the projected use of efficient irrigation techniques ac-\ncordingtoourdataset(dripandsprinkleraccountingfor∼2/3in2100,upfrom\n∼1/9 in 2015) (Figure 1a). Under SSP3-7.0, projected irrigated land31 increases\nsteadily,reachingmorethan4×106km2bytheendofthecentury,withasmaller\nfractionofdripandsprinklertechniques(lessthan1/2in2100)(Figure1b).\nBased on the spatial distribution of AEI and ITS (Figure 2, 3, and S1-2), we\nselect 13 IPCC reference regions32 with high irrigation activity or with varying\nchangesinAEIundertwoscenarios(Figure4a),andpoolthemintothreegroups\n(Table S1). Group 1 includes regions (solid line in Figure 2a) with higher socio-\neconomic capacities, like Central North America and East Asia, enabling AEI to\nremain nearly constant and efficient irrigation to grow rapidly under both sce-\nnarios (slightly faster under SSP1-2.6, see Figure 1c,d and S3). Group 2 consists\noftheregions(dashedlineinFigure2a)withlargeAEIbutlowersocio-ecomonic\ncapacitiesinpresent-day,suchasSouthAsiaandWestCentralAsia. InGroup2,\nAEIremainsrelativelyconstantunderSSP1-2.6butexpandsrapidlyunderSSP3-\n7.0 (Figure 1e,f and S4). The regions in Group 3 (dotted line in Figure 2a) have\nalmost no AEI historically and under SSP1-2.6, but their AEI experiences sub-\nstantial expansion under SSP3-7.0, making them irrigation hot spot regions by\nthe end of the century. These future hotspot regions for example include West\nandSouthAfrica(Figure1g,handS5).\nIWW is highly related to spatial and temporal changes in both AEI and ITS\n(Figure4). From1985to2014, NorthIndiawasthemostintenselyirrigatedarea\nwithsimulatedIWWsurpassing500mmyr−1 insomegridcells(overtheentire\n0.9°×1.25°gridcellarea). OtherheavilyirrigatedareasincludedEastChinaand\nCentral USA, where simulated IWW also exceeds 300 mm yr−1 in several grid\ncells (Figure 4a). Globally, IWW ranges between 1700 − 2000 km3 yr−1 during\nthehistoricalperiod(inhistoricalsimulations,AEIandITSisstaticatthelevelof\ntheyear2010),andIWWprojectionsstartfromaround2100km3yr−1 intheyear\n2015(Figure4d).\nIWWisprojectedtodecreaseinmanygridcellsoverCentralNorthAmerica\nandEastAsiaunderSSP1-2.6(2045-2074)comparedtothehistoricalperiod(1985-\n2014), where the reduction exceeds 50 or even 100 mm yr−1 locally (Figure 4b).\nGlobal IWW decreases slightly to ∼1700 km3 yr−1 by 2074 (Figure 4d), with the\nregional values falling from ∼400 to ∼300 km3 yr−1 in Group 1 (Figure 4e and\nS6a-c), from ∼1300 to ∼1100 km3 yr−1 in Group 2 (Figure 4f and S6d-h), and\nremainingconstantataround50km3yr−1 inGroup3(Figure4gandS6i-m).\nUnder SSP3-7.0 (2045-2074), the reduction in IWWpersists in Central North\nAmerica and East Asia, but new irrigation hot spots appear across Africa, and\nIWWincreasesinsometraditionalirrigationhotspotslikeinSouthAsia(Figure\n4c). After 2015, IWW increases continuously to ∼2400 km3 yr−1 under SSP3-\n7.0 (Figure 4d). IWW shows different trends across the three region groups due\n1. Reviewer1 13\nto the varying socioeconomic developments in each group. In Group 1, IWW\ndecreasestolessthan300km3yr−1 (Figure4eandS6a-c),owingtoasteadyAEI\nand improvements in ITS (Figure 1c,d and S3). In Group 2, IWW increases to\n∼1400 km3 yr−1 (Figure 4f and S6d-h), mainly attributed to the expanding AEI\n(SSP3-7.0) (Figure 1e,f and S4). In Group 3, a sharp increase from 50 to 400 km3\nyr−1 in IWW is projected (Figure 4g and S6i-m) due to a rapid AEI expansion\n(Figure1g,handS5).\nReviewer1Comment13\nLines282-288: Iwouldsaythatfarmers’individualdecision-makingandgovernment\nregulationarepartofthesocioeconomicconditions. ButIcanbewrong.\nResponse Yes, we agree. Farmers’ individual decision-making and government regulation\ncan be largely affected by socioeconomic conditions. We therefore have modified the sen-\ntence.\nL342-345\nIn reality, farmers’ individual decision-making and government policies are\nnot fully determined by socio-economic capacities. For example, farmers’ de-\ncisions to improve irrigation efficiency are motivated by multiple factors, espe-\nciallythecostandbenefits.\nReviewer1Comment14\nLines270-305: LimitationsareusuallydiscussedattheendoftheDiscussionsection.\nResponse We thank Reviewer 1 for this advice. The Discussion Section has been restruc-\ntured,andnowthelimitationsarethelastpartofit.\nReviewer1Comment15\nLines 583-586: Please explain here why the authors focused on these two extreme\nSSPs, i.e. SSP1 and SSP3 (not considering SSP5). Moreover, in the main text the au-\nthors focus on SSP1 and SSP3. It is a bit confusing that results are shown for SSP5\nalso, but not really considered in the main document. I suggest to remove this from\nthemanuscriptorincorporatetheresultsinthemaintext.\nResponseWeapologiseforthisconfusion. WehavemovedtheresultsunderSSP5-8.5from\nmaintextstoSupplementaryInformation.\nReviewer1Comment16\nLines620-632: Isuggesttoincludeaflowcharttomakethiseasiertounderstand.\nResponse Thansk for this comment. The flowchart has been added in Supplementary Fig-\nures(seeFIG2),andaparagraphrepresentinganexampleisgiven.\nL679-693\nI. Check if there is irrigated No\ncropland in this grid cell\nYes\nIV. Check in which country or region is this grid\nII. Get the historical irrigation techniques share.\ncell.\nV. Get the values of socio-economic variables of\nIII. Calculate the average precipitation during the\nthe country or region and do principal component\nprevious 20 years in this grid cell and normalise it\nanalysis. Then normalise the first value based on\nbased on historical values.\nthe historical value.\nVI. Adjust the changing rate of irrigation techniques share based on 20-year\naverage precipitation and the first variable of socio-economic variables\nafter the principal component analysis.\nVII. Reduce the flood irrigation based on the adjusted changing rate, and\nNext grid cell\nthen assign them to drip and sprinkler irrigation based on crop types.\nFIG2: Illustrativeflowchardforirrigationtechniquessharechange.\nGiven the complexity of the process, a representative example is presented\nforillustrativepurposes(aflowchartisalsogivenasshowninFigureS8). Inthe\nyear 2075, a grid cell is assumed to contain 70% flood irrigation, 20% sprinkler\nirrigation, and 10% drip irrigation. The crop type distribution is assumed to be\n35%floodcrops,50%sprinklercrops,and15%dripcrops(seeTableS4). Thepro-\ncedurebeginsbycalculatingthenormalised20-yearaverageprecipitationforthe\nperiod 2056–2075, which is assumed to be 0.45. Subsequently, the first principal\ncomponentofthesocio-economicindicators—GDP,GOV,URB,andGII—iscom-\nputed using the PCA model previously developed from historical SSP datasets,\nyielding a normalised value of 1.25. The annual updating rate is determined by\nsubtractingthehydro-climaticadjustingfactor(0.1)from1andaddingthesocio-\neconomicadjustingfactor(0.8)(TableS4),resultinginarateof1.7%peryearover\nthesubsequentfive-yearperiod(until2080)(TableS5). Accordingly,by2080,\nthe\nflood irrigation fraction is reduced by 8.5% (1.7% × 5 years), with the reduction\nredistributedtosprinkleranddripirrigationinproportionsof6.54%and1.96%,\nrespectively. Thus,by2080,thesharesofflood,sprinkler,anddripirrigationare\nadjustedto61.5%,26.54%,and11.96%,respectively.\n1. Reviewer1 15\nReviewer1Comment17\nLine671: PleasegivethedefinitionofNOI.\nResponseWeapologisefortheconfusion. Wehaverewrittenthissentence.\nL725-727\nWe first conduct the two historical simulations with (Hist_IRR) and without\nirrigation (Hist_NOI) covering the period 1980-2014, with first five years as the\nspin-up period, using the land use and land management of the year 2010, and\nincludingtheexistingITSdataset(Figure8).\n1.3 Remarksoncodeavailability\nReviewer1Comment18\nAREADMEfileisprovided,butonlygiveslimitedinformationonwhatthedifferent\nscriptsaccomplish.\nIngeneral,mostscriptsdonotincludecommentsonthefunctionalityofthescript. It\nisthereforedifficulttounderstandwhatthecodeismeantfor. Moreover,thescriptare\nnot particularyDRY (Don’t RepeatYourself), there isa lot ofrepitition, which makes\nthe code difficult to understand. Also the code is written in three different program-\nminglanguages(Matlab,Pythonandshellscript),whichisofcoursenotideal.\nResponseThanksforpointingitout! WehaverewrittenthecodesinPython,improvedthe\ncodes to avoid repitions, and also uploaded the codes developed for CESM2 and for plot-\nting. Please check the github respository https://github.com/YiYao1995/Yao_et_\nal_2025_Nature_Communications_Compounding_Future_Escalation.git.\n2.1 Majorcomments\nReviewer2Comment1\nThis is an interesting article that investigates the irrigation impacts on extreme dry\nand humid heat under future scenarios of changes in irrigation extent and irrigation\nefficiency. The authors set up different SSP-RCP pathway scenarios to develop dy-\nnamicglobalirrigationtechniquesshare(ITS)datasetsandembedthemintheCESM2\nModel,andconductsensitivesimulationexperimentswithCESM2toassessirrigation\nwaterwithdrawandquantifyirrigationimpactsonextremedryandhumidheatun-\nder dynamic conditions of changing irrigation extent and efficiency. The innovation\nof this study is the development of the dataset that considers irrigation technology\nchange using a combination of SSP and ISIMIP3 scenarios embedded in the CESM2\nfor annual updates of the global ITS. In the context of increasing global heat stress,\nthisresearchtopichasreceivedmuchattentionrecently,andthisstudyhighlightsthe\nimportanceofsustainablesocio-economicpathwaysandefficientirrigationtechnolo-\ngiestoprovidemoresolidevidenceonthoseissues. Themanuscriptisclearlywritten,\ntheresultsarewellpresentedthroughseveralhigh-qualitytablesandgraphs,overall\nI would recommend the manuscript for publication in Nature Communications but\nhaveafewcommentsthatmightbeconsideredinarevisedversionofthemanuscript:\nResponse\nWe sincerely thank Reviewer 2 for the encouraging feedback and valuable suggestions,\nwhichhavecontributedtoimprovingourmanuscript. Below,weprovidedetailedresponses\ntoeachcomment.\nReviewer2Comment2\nCurrentstudywasconductedbasedonacombinationofSSP1,SSP3withRCP2.6,7.0\nscenarios, and the fact that the findings are very sensitive to the choice of SSP-RCP\nscenarios, a discussion of the uncertainty in the results due to the choice of different\npathwaysseemstobenecessary.\nResponse Indeed, the results are strongly sensitive to the choice of SSP-RCP scenarios, so\nweaddedsometextsinthemaintext.\nL251-258\nIn this study, we, for the first time, design ITS scenarios aligned with the\nSSP-RCPs, improve an Earth system model to incorporate this information, and\nperform fully-coupled climate simulations under varying climate and irrigation\ntransitionpathways. Wethenanalysethesimulationoutputsforirrigationwater\ndemandandirrigation-inducedimpactsondryandmoist-heatstress. Theresults\nexhibitastrongsensitivitytothechoiceofSSP-RCPscenarios,highlightingtheir\nimportant implications for future irrigation planning and development. While\nthesescenariosrelyonseveralidealisedassumptions, theunderlyingnarratives\n2. Reviewer2 17\nstilloffervaluableinsightsforguidingfutureirrigationstrategies.\nL371-376\nGenerally, the results exhibit a strong dependence on the selected scenarios,\nwith this study focusing exclusively on SSP1 and SSP3. These scenarios rep-\nresent two contrasting extremes in terms of climate mitigation and adaptation\nrisks. However, they may no longer reflect the most current socioeconomic and\nclimate projections, as newer scenarios have since been developed to address\ncontemporaryresearchquestions. Futureirrigation-relatedstudiesshouldthere-\nforeincorporateupdatedscenarioframeworkstoyieldmorerelevantinsightsfor\npolicyandplanning.\nReviewer2Comment3\nI agree with the key hypothesis that “richer and drier countries will invest more in\nhigh-efficient irrigation”. For the East Asia region, with the irrigated area remaining\nessentially unchanged, IWW has been reduced by about 30-40% through improve-\nments in irrigation water use efficiency as irrigation technology has evolved (esti-\nmated from Figure 2). In East Asia, China has the most potential and motivation\nto improve irrigation efficiency. In the case of China, for example, the current\nirrigation water use efficiency is about 0.56, and the results in the manuscript suggest\nthat the water use efficiency would need to be raised to nearly 1 in the future, which\nconfuses me. Consideration of irrigation efficiency threshold constraints in the ITS\ndatasetseemsnecessary,ifpossible.\nResponse We agree that this is a very confusing point. In this study, we do not directly\nproject the change in irrigation water use efficiency, but rather the relative fraction of three\ndifferentirrigationtechniques. InCESM2,thehydrologicalprocessesaftertheapplicationof\nirrigationaresimulatedbyitshydrologymodule,whichmeansthattheirrigationefficiency\nisdependentonmanyfactors,likebackgroundclimate,soiltype,croptype,etc. Evenforthe\nsame irrigation technique, the irrigation efficiency can be different. In this study, following\nJagermeyr et al. (2015) (3), we divided all crop types into three groups based on the suit-\nability of irrigation techniques (TAB 2), which kind of already defines the upper threshold\nofhigh-efficiencyirrigation(sprinkleranddrip).\nAt the same time, the increase in irrigation efficiency can also be triggered and boosted by\nother factors rather than socio-economic and hydro-climatic conditions. The classification\nframeworkusedinthisstudyisveryidealised,sowealsoaddedsometextsinthediscussion\nsection.\nL341-353\nRegardingtheassumptionsappliedfortransitionsinITS,onlysocioeconomic\nand hydro-climatic conditions are considered as drivers. In reality, farmers’ in-\ndividual decision-making and government policies are not fully determined by\nsocio-economiccapacities. Forexample,farmers’decisionstoimproveirrigation\nefficiencyaremotivatedbymultiplefactors,especiallythecostandbenefits.\nAn-\nother issue is that the socio-economic capacity variables are assumed to be the\nTAB2: SuitableirrigationtechniquesfordifferentcroptypesinCESM2inputdata\nCroptype drip sprinkler flood\nSpringwheat No Yes Yes\nWinterwheat No Yes Yes\nBarley No Yes Yes\nWinterbarley No Yes Yes\nRye No Yes Yes\nWinterrye No Yes Yes\nRice No No Yes\nTemperatemaize No Yes Yes\nTropicalmaize No Yes Yes\nMillet No Yes Yes\nSorghum No Yes Yes\nPulses Yes Yes Yes\nCassava No No No\nSunflower Yes Yes Yes\nTemperatesoybean Yes Yes Yes\nTropicalsoybean Yes Yes Yes\nGroundnuts No Yes Yes\nRapeseed No Yes Yes\nSugarcane No Yes Yes\nCitrus Yes Yes Yes\nCocoa Yes Yes Yes\nCoffee Yes Yes Yes\nCotton Yes Yes Yes\nDatepalm Yes Yes Yes\nGrapes Yes Yes Yes\nOilpalm Yes Yes Yes\nPotatoes Yes Yes Yes\nFoddergrass No Yes Yes\nMiscanthus No Yes Yes\n2. Reviewer2 19\nsame within each country, ignoring the internal variability within bigger\ncountries. Moreover,a1%defaultrateoffloodirrigationreductionisassumedforall\ncountries based on historical time series of two countries (see Method), which\nmaynotberealisticforothercountries, andchangingthisdefaultratemaylead\nto different results. Furthermore, precipitation is the only variable used to indi-\ncatehydro-climaticcomplexity,butsomeothervariables,likegroundwateravail-\nability, river discharge should also be included if data availability permits. At\nthesametime,thefeedbackofsocio-economiccapacitiestoirrigationtechniques\nsharechanges,shouldalsobeconsideredinfuturework.\nReviewer2Comment4\nInthisstudy,itisassumedthattheevolutionofdripandsprinklerirrigationtoreplace\nflood irrigation is at a constant rate of 1%, and as you mentioned, paddy irrigation\nis included in flood irrigation, and planting rice with drip and sprinkler irrigation\ntechniques seems technically challenging currently. Especially in East Asia, where\nricecultivationisdominant,willthisunderestimatetheirrigationimpactsonextreme\ndryandhumidheatinfuturescenarios?\nResponseWearesorryfortheconfusion. Inthisstudy,paddyirrigationisappliedbydefault\nwhen it is rice, but when presenting the irrigation techniques share, it is counted as flood\nirrigation. Weaddedsometextsinthediscussion.\nL334-339\nMoreover, in this baseline dataset, flood irrigation and paddy irrigation are\nnot distinguished as separate categories. Instead, paddy irrigation is included\nunder the broader classification of flood irrigation, despite the practical differ-\nences between the two methods in actual implementation. In the future, more\ninformation should be collected through the national agricultural census, and\ntheclassificationmethodstodistinguishdifferentirrigationtechniquesbasedon\nremotesensingdataneedtobedeveloped.\nReviewer2Comment5\nCESM2 has no limits on irrigation withdrawals for the irrigation module, and in fact\nirrigationwithdrawalsarelimitedbytheregionalriverflows,andforthefuturenew\nhotspotsmentionedinthemanuscript,whethertheunconstrainedapplicationofirri-\ngationwithdrawalswouldoverestimatetheirrigationimpactsontheextremeheatin\ntheregionrepresentedbytheMiddleEastandAfrica.\nResponse We thank Reviewer 2 for pointing this limitation out and we have added some\ntextintheDiscussionsection.\nL285-287\nInthisstudy,wateravailabilityisnotactivated,sothesimulatedIWWmostly\nindicatesirrigationdemand,andtherequirementforwatermaythereforenotbe\nfulfilled,leavingcropyieldsindanger.\nL349-351\nFurthermore,precipitationistheonlyvariableusedtoindicatehydro-climatic\ncomplexity, but some other variables, like groundwater availability, river dis-\nchargeshouldalsobeincludedifdataavailabilitypermits.\nReviewer2Comment6\nLines 219-222 illustrate that irrigation practices in traditionally highly irrigated ar-\neas have minor impacts on extreme dry heat under the future scenarios, this point\ndeserves more in-depth comparative analysis and discussion. The comparisons in\nFigures4candfseemtocompareextremedryheatdifferencesbetweendifferentSSP-\nRCPpathwaysratherthantheimpactofirrigation.\nResponse Thanks to Reviewer 2 for this suggestion. We added some text to highlight this\npointintheDiscussionsection.\nL302-312\nUnder both SSP1-2.6 and SSP3-7.0, increased greenhouse gas emissions in-\ncreasethefrequencyofextremedry-heatevents(Figure6a,c). Irrigationhasbeen\nproposedasaclimate-effectivelandmanagementstrategy,andhasbeenfoundto\nsloworevenreverselocalwarmingtrendsinrecentdecades. However,although\nthe cooling impacts of irrigation on extreme dry-heat persist in projections (Fig-\nure 5e,g), we find that irrigation will no longer be able to create cooling islands\nin the future as effectively as it did in the past. This could be attributed to the\npronouncedimpactsofclimatechange,aswellastheupgradeinirrigationtech-\nniques over some traditional irrigation regions, as less water will be applied to\nfields. Notably, the effects of irrigation on dry-heat extreme frequency will be\nmuch smaller compared to the impact of following different SSP-RCPs (Figure\n5e,gand6e),intermsofbothmagnitudeandspatialcoverage.\nWe also restructured the Results section and removed the comparison between scenarios\nwithoutirrigation(SSP1_NOIandSSP3_NOI).\n2. Reviewer2 21\nReviewer2Comment7\nAsyoumentionedinline315,someareasthatarecurrentlyconsideredunsustainable\nfor irrigation practices continue to experience an increase in irrigation water with-\ndrawalduetoirrigationexpansionunderfuturescenarios,andtheirviabilitydeserves\nfurthervalidationanddiscussion.\nResponseWeadmitthisisasignificantlimitationinthisstudy, andwethereforeaddsome\ntextintheDiscussionsection(seealsoReviewer2Comment5).\nL362-365\nTofurtherimproveirrigationrepresentationinEarthsystemmodels,newre-\nsearchcouldaimatcollectingandcalibratingcrop-andirrigation-relatedparam-\netersregionally,implementingcroprotationsandmultiplecroppingseasons,and\nincorporatinggroundwateravailabilityandevolution.\nReviewer2Comment8\nThisstudyisacontinuationoftheauthors’recentachievement(DOI:10.1038/s41467-\n025-56356-1),whichhasin-depthinsightsinthisfield,andIbelievethatiftheycould\nconsider how to guide the actual irrigation patterns towards the most beneficial di-\nrection for human based on the previous quantitative analysis and mechanism ex-\nploration studies of irrigation impacts on heat stress in the discussion section, it will\nprovide a solid theoretical basis for the climate change adaptation strategies in agri-\nculture.\nResponse Thanks to Reviewer 2 for this suggestion. We added some text in the Discussion\nsection.\nL251-258\nIn this study, we, for the first time, design ITS scenarios aligned with the\nSSP-RCPs, improve an Earth system model to incorporate this information, and\nperform fully-coupled climate simulations under varying climate and irrigation\ntransitionpathways. Wethenanalysethesimulationoutputsforirrigationwater\ndemandandirrigation-inducedimpactsondryandmoist-heatstress. Theresults\nexhibitastrongsensitivitytothechoiceofSSP-RCPscenarios,highlightingtheir\nimportant implications for future irrigation planning and development. While\nthesescenariosrelyonseveralidealisedassumptions, theunderlyingnarratives\nstilloffervaluableinsightsforguidingfutureirrigationstrategies.\nL291-295\nLimiting unsustainable irrigation expansion and enhancing irrigation effi-\nciencyare,therefore,crucialformitigatingwaterscarcity. Achievingthisrequires\nathoroughassessmentofcurrentandprojectedhydrologicalconditionspriorto\nselecting areas for cropland expansion, as well as the development and deploy-\nment of high-efficiency irrigation technologies, particularly in regions already\nfacingwaterstress.\n2.2 Minorcomments\nReviewer2Comment9\nTheabbreviationofFigure4oncomparingthedifferencesbetweenfutureandhistor-\nicalscenariosfailstoconveyaclearenoughmeaning\nResponseThanksforpointingitout. WehaverestructuredtheResultssectionandchanged\nthecaptions. PleasecheckthecaptionsofFigure5and6inthemaintext.\nReviewer2Comment10\nProvide formulas for key evaluation indicators, such as wet bulb temperature, in the\nmethodologysectionforbetterindependentunderstanding.\nResponseThanksforthissuggestion. WehaveaddedittoMethods.\nReviewer2Comment11\nThe figures and tables in the Appendix would be better as Support Information for\nthemanuscript.\nResponseThanksfortheadvice. Wehavemovedthemtothesupplementarymaterial.\nReviewer2Comment12\nDriving CESM2 simulations using SSP-RCP scenarios and innovative ITS datasets is\nthecoreofthisstudy, andthenecessarydescriptionsoftheCESM2modelingrelated\nsetupintheMethodssection(orsupportinformation)willmakethepapermoreper-\nfect.\nResponseThankyouforthisadvice. WehaveaddedsometextintheMethodssection.\nL703-708\nTo allow three different irrigation techniques for each CFT per grid cell, we\nexpand the cropland unit to 128 columns, one rainfed, one drip irrigated, one\nsprinkler irrigated, and one flood irrigated for each CFT. The calculations of the\nlandfluxesareconductedindividuallyovereachcolumnbeforebeingaveraged\nto the grid cell level. Transient ITS is implemented through the preparation of\ninput land use and management time series, in which we provide the annual\ninformationofITSfromtheyear2015to2100.\nL737-739\nIn CESM2, when transient land-use is activated, the surface map is updated\nyearly, and the ITS is incorporated in the surface map, which enables the model\ntousetransientITS.\n3. Reviewer3 23\n3.1 Majorcomments\nReviewer3Comment1\nThe study “Compounding future escalation of emissions- and irrigation-induced in-\ncreasesinhumid-heatstress”discussesdifferentirrigationscenariosandtheirimpact\non various heat events. It is a valuable contribution to understanding the feedback\nbetween irrigation methods and local heat stress. I thoroughly enjoyed reading the\npaper. Ihaveafewcommentsthatmayhelpexpandthediscussion, whicharelisted\nbelow.\nResponse We thank Reviewer 3 for these supportive words and comments, which help us\ntoimprovethemanuscript. Below,weaddressallcommentsonebyone.\nReviewer3Comment2\nWhatiftheassumedITStransitionrateof1%peryearisnotaccurate? Canyoubriefly\ndescribe what would happen if the ITS rate were different, and what the plausible\nscenariosmightbe?\nResponse Thanks for pointing this out. Indeed, this 1% per year rate is set based on the\navailableirrigationtechniquessharetimeseriesfromtwocountries,theUSAandIran,soit\nmaybeunrealistictoassignsuchavalueforallcountries. Thedefaulttransitionratedirectly\ndetermines the time it takes for every country to achieve the maximum drip and sprinkler\nirrigation fraction, which is based on the distribution of local crop types, as shown in TAB\n3.\nWealsoaddedsomediscussioninthemaintext.\nL341-353\nRegardingtheassumptionsappliedfortransitionsinITS,onlysocioeconomic\nand hydro-climatic conditions are considered as drivers. In reality, farmers’ in-\ndividual decision-making and government policies are not fully determined by\nsocio-economiccapacities. Forexample,farmers’decisionstoimproveirrigation\nefficiencyaremotivatedbymultiplefactors,especiallythecostandbenefits.\nAn-\nother issue is that the socio-economic capacity variables are assumed to be the\nTAB3: Suitabilityofirrigationtechniquesofeachcroptype\nCroptype drip sprinkler flood\nSpringwheat No Yes Yes\nWinterwheat No Yes Yes\nBarley No Yes Yes\nWinterbarley No Yes Yes\nRye No Yes Yes\nWinterrye No Yes Yes\nRice No No Yes\nTemperatemaize No Yes Yes\nTropicalmaize No Yes Yes\nMillet No Yes Yes\nSorghum No Yes Yes\nPulses Yes Yes Yes\nCassava No No No\nSunflower Yes Yes Yes\nTemperatesoybean Yes Yes Yes\nTropicalsoybean Yes Yes Yes\nGroundnuts No Yes Yes\nRapeseed No Yes Yes\nSugarcane No Yes Yes\nCitrus Yes Yes Yes\nCocoa Yes Yes Yes\nCoffee Yes Yes Yes\nCotton Yes Yes Yes\nDatepalm Yes Yes Yes\nGrapes Yes Yes Yes\nOilpalm Yes Yes Yes\nPotatoes Yes Yes Yes\nFoddergrass No Yes Yes\nMiscanthus No Yes Yes\n3. Reviewer3 25\nsame within each country, ignoring the internal variability within bigger\ncountries. Moreover,a1%defaultrateoffloodirrigationreductionisassumedforall\ncountries based on historical time series of two countries (see Method), which\nmaynotberealisticforothercountries, andchangingthisdefaultratemaylead\ntodifferentresults.\nReviewer3Comment3\nDoublingthenumberofcroplandcolumnsfrom64to128increasesmodelcomplexity\nandcomputationalcost. Ifthedatausedtoallocateirrigationmethodsacrosscolumns\nisuncertainorbasedonassumptions,howdoyouensurethatthisaddedcomplexity\nresults in more reliable outcomes? Can you also discuss the limitations of the model\nused?\nResponseYesthisisagoodpoint. Althoughthelandmodelconsumesfewercomputational\nresources than the atmosphere model, increasing the number of basic calculation units still\nincreases its complexity. In this study, we expanded the model to represent time-varying\nirrigationtechniquesshare,butwhetheritisstillworthitrequiresevaluationandvalidation.\nThus,weaddsometextstothediscussion.\nL365-369\nAt the same time, the increased complexity of the model in this study (crop-\nlandcolumnsexpandedfrom64to128)requiresmorecomputationalresources,\nunderscoringtheimportanceofevaluatingthenewmoduleonreproducingsur-\nface fluxes. Previous evaluations were mainly focused on a single-point scale,\nandinthefuture,thevalidationatregionalandlocalscalesshouldbeconducted.\nReviewer3Comment4\nCould you elaborate on the assumptions underlying the hydro-economic frame-\nwork? Forinstance: i)Classifyingcountriesbynational-levelvariablesmisseswithin-\ncountryvariabilityinbothirrigationpracticesanddrivingfactorsii)Sincethedriver\nselectionisbasedonSpearman’scorrelation, howdoyouaddressthefactthatcorre-\nlationdoesnotimplycausation? iii)Whyisprecipitationusedasthesoleindicatorof\nhydro-climaticconditions,andhowdoyouaccountforotherkeyfactorslikeground-\nwateravailability,riverdischarge,infrastructure,orwaterrights? iv)Theframework\nisstaticandscenario-based: itprojectsfutureITSbasedonfixeddriversbutdoesnot\nsimulate feedbacks (e.g., how changes in ITS might influence GDP, water stress, or\ngovernanceovertime).\nResponse Sorry for not clearly explaining the whole process of irrigation techniques share\nprojectionandthelackofacomprehensivediscussionregardingthis.\n(1) Indeed, the internal variabilities of socio-economic variables are ignored, so we added\nsometextsintheDiscussionsection.\nL345-346\nAnother issue is that the socio-economic capacity variables are assumed to\nbe the same within each country, ignoring the internal variability within bigger\ncountries.\nii)Indeed,correlationdoesnotimplycausation,butwedobelievethathighersocio-economic\ncapacitiesarebeneficialforirrigationtechniquesupgrade. Ofcourse, thedatasetgenerated\nhere is highly idealised, but the plan is to generate a dataset aligning with the narratives\nundertheSSPframework.\nWeaddedaparagraphintheDiscussionsection.\nL260-273\nThe assumptions regarding land use and agriculture under SSP1 and SSP3\nare provided as \"strong regulations to avoid environmental tradeoffs; improve-\nmentsinagricultureproductivity;rapiddiffusionofbestpractices\",and\"Hardly\nany regulation; continued deforestation due to competition over land and rapid\nexpansion of agriculture; Low technology development, restricted trade\". The\nLUH2 dataset agrees with those narratives, with the key feature that AEI keeps\nalmostconstantunderSSP1-2.6andexpandsrapidlyunderSSP3-7.0(Figure1,2).\nSome assumptions regarding the irrigation water use efficiency have also been\nproposed, with \"large improvements in irrigation water use efficiency where\npossible\" for SSP1 and \"only modest improvements in irrigation water use ef-\nficiency\" for SSP3. The generated ITS data align well with those assumptions,\nwitharapiddecreaseintraditionalinefficientfloodirrigationunderSSP1-2.6and\na slower decrease under SSP3-7.0 (Figure 1, 3). Although we apply a simplified\nadjustment factor to account for annual ITS changes based on socio-economic\nand hydro-climatic variables (see Methods), the resulting estimates are consis-\ntent with prevailing assumptions, thereby offering a valuable enhancement to\nexistingdatasets.\niii)Becauseprecipitationistheonlyvariablewhichshowssignificantcorrelationwithirriga-\ntiontechniquesinthisstudy(TableS2). Otherfactorsarenotquantifiedinthisstudy,atleast\nnot available from ISIMIP simulations, but we believe that infrastructure and water rights\ncanbeincludedinthesocio-economiccapacitiesinfuture.\nWethereforeaddedasentenceintheDiscussionsection.\nL349-351\nFurthermore,precipitationistheonlyvariableusedtoindicatehydro-climatic\ncomplexity, but some other variables, like groundwater availability, river dis-\nchargeshouldalsobeincludedifdataavailabilitypermits.\n(iv) Yes, there is no feedback included, as socioeconomic scenario is typically treated as an\ninput to the ESM and thus not explicitly modelled. However, these feedbacks should be\nconsideredinfutureworkwhereacloserinteractionofESMsandIAMscouldbeexplored.\nWethereforeaddedasentenceintheDiscussionsection.\nL351-353\nAtthesametime,thefeedbackofsocio-economiccapacitiestoirrigationtech-\nniquessharechanges,shouldalsobeconsideredinfuturework.\nREFERENCES 27\nReferences\n[1] O’Neill,B.,Kriegler,E.,Ebi,K.,Kemp-Benedict,E.,Riahi,K.,Rothman,D.,VanRuijven,\nB., Van Vuuren, D., Birkmann, J., Kok, K. & Others The roads ahead: Narratives for\nshared socioeconomic pathways describing world futures in the 21st century. Global\nEnvironmentalChange.42pp.169-180(2017)\n[2] Wada, Y., Flörke, M., Hanasaki, N., Eisner, S., Fischer, G., Tramberend, S., Satoh, Y.,\nVanVliet,M.,Yillia,P.,Ringler,C.&OthersModelingglobalwateruseforthe21stcen-\ntury: TheWaterFuturesandSolutions(WFaS)initiativeanditsapproaches.Geoscientific\nModelDevelopment.9,175-222(2016)\n[3] Jägermeyr,J.,Gerten,D.,Heinke,J.,Schaphoff,S.,Kummu,M.&Lucht,W.Watersav-\nings potentials of irrigation systems: global simulation of processes and linkages. Hy-\ndrologyAndEarthSystemSciences.19,3073-3091(2015)"}, {"round": 2, "reviewer_comments": {"reviewer_1": "Review of “Compounding future escalation of emissions- and irrigation-induced increases in humid-heat stress” submitted to\nNature Communications for consideration for publication.\nThe manuscript has been revised and many improvements have been made. This includes a restructuring and better\ndescription of the results. Moreover, the discussion section has been revised and restructured. These changes have\nincreased the readability of the manuscript. Apart from the minor comments below, I think the manuscript is acceptable for\npublication.\nBelow I have provided specific comments to the text, figures and tables.\nSpecific comments\nLines 41-45: Please revise this sentence. I think the previous version said correctly “increases”, which now was changed to\n“increase” (line 42). Moreover, it is unclear to what “them” refers to in line 43.\nLines 93-96: I still don’t understand why the authors consider the SSP5 scenario here. The main document does not include\nany reference to this scenario, so why mention it here? Again, I highly suggest to remove the SSP5 scenario altogether from\nthe manuscript (also from the Methods section).\nLine 180: Please replace “is increased” by “has increased”.\nLine 182: I’m not sure if “intensify” is correctly used here. Maybe better say “increase” or “amplify”.\nLines 241-244: These results consider the traditional hotspot areas? Please indicate in the text.\nLines 249-250: I’m not so convinced about the difference between the scenarios with and without irrigation. In Figure 7 these\ndifferences are not that big for most of the data shown. Only for dry-heat for the traditional irrigation hotspots this difference\nseems rather obvious, but for the other scenarios not that much. Please change this sentence accordingly.\nLines 260-264: Please explicitly indicate which assumption belongs to which SSP scenario.\nLines 327-369: These three paragraphs include many limitations to the presented methods. The authors propose some\nchanges to the existing method. However, it would be interesting to read how these proposed methods would affect the\nresults. So, instead of including a longer list of improvements, it would be interesting to select the most relevant and describe\nhow these improvements might affect the results.\nLines 371-376: I suggest to end the Discussion section with a paragraph with some concluding remarks, instead of the\nlimitations of the study.\n(Remarks on code availability)\nThe authors included comments to the code so it can be better understand by the interested readers.", "reviewer_2": "We think the revision is detailed and effective. So we recommend acceptance of the manuscript.\nYuanyuan Zha and Liangfeng Zou at Wuhan University\n(Remarks on code availability)\nWe checked the code last time.", "reviewer_3": "The authors have provided substantial additional discussion in the manuscript, effectively addressing several of the queries I\nraised. The revised version presents a clearer and more comprehensive picture of the work undertaken and the aspects to\nbe addressed in the future. I am satisfied with the manuscript in its current form.\n(Remarks on code availability)"}, "author_response": "Contents\n1.1 Majorcomments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4\nReviewer1Comment1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4\n1.2 Minorcomments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4\nReviewer1Comment2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4\n1\nReviewer1Comment3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4\nReviewer1Comment4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4\nReviewer1Comment5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4\nReviewer1Comment6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5\nReviewer1Comment7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5\nReviewer1Comment8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5\nReviewer1Comment9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6\nReviewer1Comment10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7\nCONTENTS 3\nAbstract\nThisresponselettercontainsnumberedfiguresandtablesandreferencestothem. To\npreventconfusion,thefiguresandtablesembeddedwithinthisresponseletterarecalled\nFIGs and TABs, while the figures and tables embedded within the manuscript are still\ncalledFiguresandTables. Thefollowingconventionisappliedtodenotemodificationin\ntheoriginalmanuscript: newtext.\n1.1 Majorcomments\nReviewer1Comment1\nThemanuscripthasbeenrevisedandmanyimprovementshavebeenmade. Thisin-\ncludes a restructuring and better description of the results. Moreover, the discussion\nsectionhasbeenrevisedandrestructured. Thesechangeshaveincreasedthereadabil-\nityofthemanuscript. Apartfromtheminorcommentsbelow,Ithinkthemanuscript\nisacceptableforpublication.\nBelowIhaveprovidedspecificcommentstothetext,figuresandtables.\nResponse We thank Reviewer 1 for these supportive words and the constructive sugges-\ntions, which has helped us to improve our manuscript. Below, we address every comment\ncarefullyandexplainthecorrespondingchangesinthemanuscript.\n1.2 Minorcomments\nReviewer1Comment2\nLines 41-45: Please revise this sentence. I think the previous version said correctly\n“increases”,whichnowwaschangedto“increase”(line42). Moreover,itisunclearto\nwhat“them”referstoinline43.\nResponseWethankReviewer1forfindingthismistake. Wehaverevisedthissentence.\nAbstract,L40-43\nMoreover, moist-heat extreme event frequency increases more substantially\n(by≥1600hoursyr−1 underSSP3-7.0intropicalregions),andirrigationfurther\namplifiesthehoursofexposure(forexampleby≥100hoursyr−1 inSouthAsia).\nReviewer1Comment3\nLines93-96: Istilldon’tunderstandwhytheauthorsconsidertheSSP5scenariohere.\nThe main document does not include any reference to this scenario, so why men-\ntionithere? Again, IhighlysuggesttoremovetheSSP5scenarioaltogetherfromthe\nmanuscript(alsofromtheMethodssection).\nResponse We thank Reviewer 1 for this suggestion. We have now completely removed the\ncontentsregardingSSP5-8.5.\nReviewer1Comment4\nLine180: Pleasereplace“isincreased”by“hasincreased”.\nResponseWethankReviewer1forfindingthismistake. Wehavecorrectedit.\n1. Reviewer1 5\nReviewer1Comment5\nLine182: I’mnotsureif“intensify”iscorrectlyusedhere. Maybebettersay“increase”\nor“amplify”.\nResponseWethankReviewer1forthissuggestion. Wehavereplaced‘intensify‘by‘amplify‘\ninthissentence,andcheckedthroughouttheentiremanuscripttomakesurethatthisword\nisnotmisused.\nReviewer1Comment6\nLines241-244: Theseresultsconsiderthetraditionalhotspotareas? Pleaseindicatein\nthetext.\nResponseWethankReviewer1forthissuggestion. Weaddsometexttothissentence.\nResults,L238-240\nOvertraditionalhotspotareas,Irrigation-inducedcoolingeffectsondry-heat\nand concurrent warming effects on moist-heat contribute to an amplified diver-\ngenceinexposurehoursbetweenthesetwotypesofextremeevents.\nReviewer1Comment7\nLines 249-250: I’m not so convinced about the difference between the scenarios with\nand without irrigation. In Figure 7 these differences are not that big for most of the\ndata shown. Only for dry-heat for the traditional irrigation hotspots this difference\nseems rather obvious, but for the other scenarios not that much. Please change this\nsentenceaccordingly\nResponseWethankReviewer1forthissuggestion. Wehaverevisedthissentence.\nResults,L246-247\nFurthermore,overintenselyirrigatedareas,irrigationplaysanimportantrole\nas an anthropogenic climate forcing, which should not be neglected in future\nstudies.\nReviewer1Comment8\nLines260-264: PleaseexplicitlyindicatewhichassumptionbelongstowhichSSPsce-\nnario.\nResponseWethankReviewer1forthissuggestion. Wehaverevisedthissentence.\nDiscussion,L248-252\nThe assumptions regarding land use and agriculture under SSP1 and SSP3\nare provided as \"SSP1, a green road; strong regulations to avoid environmental\ntradeoffs;improvementsinagricultureproductivity;rapiddiffusionofbestprac-\ntices\", and \"SSP3, a rocky road; hardly any regulation; continued deforestation\ndue to competition over land and rapid expansion of agriculture; Low technol-\nogydevelopment,restrictedtrade\"(1).\nReviewer1Comment9\nLines 327-369: These three paragraphs include many limitations to the presented\nmethods. The authors propose some changes to the existing method. However, it\nwould be interesting to read how these proposed methods would affect the results.\nSo,insteadofincludingalongerlistofimprovements,itwouldbeinterestingtoselect\nthemostrelevantanddescribehowtheseimprovementsmightaffecttheresults.\nResponse We thank Reviewer 1 for this suggestion and have added one sentence for each\nparagraphtoindicatetheeffectsoftheseimprovements.\nDiscussion,L316-314\nIn this study, we estimate the changes in irrigation techniques under socioe-\nconomicandgreenhousegasconcentrationscenarios,drawingonthedifferences\nin socioeconomic development stage and baseline aridity level of every country\n(seeMethods). However,uncertaintiesremaininthisdatasetbecauseofboththe\nlackofobservationaldataandtheassumptionsapplied.\nThebaselineglobalgrid-\nddeddatasetforpresent-dayirrigationtechniquesdistribution(2)isnotadirectly\nobserveddataset. Instead,itwasgeneratedusingadecisiontreealgorithmbased\noncountry-leveldatafrommultiplesources(3;4;5), furtherinformedbyglobal\ncroplandmaps. Moreover,inthisbaselinedataset,floodirrigationandpaddyir-\nrigationarenotdistinguishedasseparatecategories. Instead,paddyirrigationis\nincludedunderthebroaderclassificationoffloodirrigation,despitethepractical\ndifferences between the two methods in actual implementation. In future, more\ninformation should be collected through the national agricultural census, and\ntheclassificationmethodstodistinguishdifferentirrigationtechniquesbasedon\nremotesensingdataneedtobedeveloped. Thiswillprovideamorerealisticba-\nsis for projecting the distribution of irrigation techniques and will facilitate the\nunderstandingofkeyfactorsforimprovingirrigationefficiency.\nRegardingtheassumptionsappliedfortransitionsinITS,onlysocioeconomic\nand hydro-climatic conditions are considered as drivers. In reality, farmers’ in-\ndividual decision-making and government policies are not fully determined by\nsocio-economiccapacities. Forexample,farmers’decisionstoimproveirrigation\nefficiency are motivated by multiple factors (6), especially the cost and benefits\n(7). Another issue is that the socio-economic capacity variables are assumed to\nbe the same within each country, ignoring the internal variability within bigger\ncountries. Moreover, a 1% default rate of flood irrigation reduction is assumed\nfor all countries based on historical time series of two countries (see Method),\nwhichmaynotberealisticforothercountries,andchangingthisdefaultratemay\nlead to different results. Furthermore, precipitation is the only variable used to\nindicate hydro-climatic complexity, but some other variables, like groundwater\navailability, river discharge, should also be included if data availability permits.\nAt the same time, the feedback of socio-economic capacities to irrigation tech-\nniques share changes, should also be considered in future work. This will sup-\n1. Reviewer1 7\nport the development of more realistic datasets on the share of irrigation tech-\nniques consistent with future pathways, enabling more accurate predictions of\nthechangesinirrigationpractice.\nThe CESM2 (8) was recently expanded to represent various irrigation tech-\nniques (9), and we here extend this functionality to capture transient changes\nin these techniques. Although the implementation of multiple irrigation tech-\nniques has led to improved performance and utility (9), limitations remain in\nthe model’s crop and irrigation representation. For example, the CESM2 has a\nglobally identical crop and irrigation technique parameterisation for each crop\ntype, and only one single cropping season is allowed per year. Furthermore, in\nthis study, the limitation of water withdrawal for irrigation is not activated, as\nthereiscurrentlynoimplementationofgroundwateravailability. Tofurtherim-\nproveirrigationrepresentationinEarthsystemmodels, newresearchcouldaim\nat collecting and calibrating crop- and irrigation-related parameters regionally,\nimplementing crop rotations and multiple cropping seasons, and incorporating\ngroundwater availability and evolution. At the same time, the increased com-\nplexity of the model in this study (cropland columns expanded from 64 to 128)\nrequiresmorecomputationalresources,underscoringtheimportanceofevaluat-\ning the new module on reproducing surface fluxes. Previous evaluations were\nmainly focused on a single-point scale (9), and in the future, the validation at\nregionalandlocalscalesshouldbeconducted. Theseeffortswillenableaccurate\nsimulation of irrigation-induced impacts on the Earth system while minimizing\nunnecessaryconsumptionofcomputationalresources.\nReviewer1Comment10\nLines 371-376: I suggest to end the Discussion section with a paragraph with some\nconcludingremarks,insteadofthelimitationsofthestudy.\nResponse We thank Reviewer 1 for this suggestion. We have moved the first paragraph of\ntheDiscussionsectiontotheendandaddedsometext.\nDiscussion,L373-388\nIn this study, we, for the first time, design ITS scenarios aligned with the\nSSP-RCPs, improve an Earth system model to incorporate this information, and\nperform fully-coupled climate simulations under varying climate and irrigation\ntransition pathways. We then analyse the simulation outputs for irrigation wa-\nter demand and irrigation-induced impacts on dry and moist-heat stress. The\nresultsexhibitastrongsensitivitytothechoiceofSSP-RCPscenarios, highlight-\ningtheirimportantimplicationsforfutureirrigationplanninganddevelopment.\nWhilethesescenariosrelyonseveralidealisedassumptions,theunderlyingnar-\nratives still offer valuable insights for guiding future irrigation strategies. More\nspecifically, under SSP1–2.6, conservative irrigation expansion combined with\nrapidadoptionofimprovedtechniquescanreduceirrigationwaterwithdrawals,\nwhereas under SSP3–7.0, massive expansion coupled with slow technological\nupgradessubstantiallyincreaseswithdrawals,generatingnewirrigationhotspot\nregions, particularly in Africa. As a scenario with higher greenhouse gas con-\ncentrations,SSP3–7.0resultsinmorefrequentdry-andmoist-heatextremesthan\nSSP1–2.6, with the increase in moist-heat exposure being more pronounced. Ir-\nrigation—particularly in intensely irrigated areas—mitigates dry-heat extremes\nbut slightly amplifies moist-heat, emerging as an important climate forcing that\nshouldnotbeoverlookedinfuturestudies.\nReferences\n[1] O’Neill,B.,Kriegler,E.,Ebi,K.,Kemp-Benedict,E.,Riahi,K.,Rothman,D.,VanRuijven,\nB., Van Vuuren, D., Birkmann, J., Kok, K. & Others The roads ahead: Narratives for\nshared socioeconomic pathways describing world futures in the 21st century. Global\nEnvironmentalChange.42pp.169-180(2017)\n[2] Jägermeyr,J.,Gerten,D.,Heinke,J.,Schaphoff,S.,Kummu,M.&Lucht,W.Watersav-\nings potentials of irrigation systems: global simulation of processes and linkages. Hy-\ndrologyAndEarthSystemSciences.19,3073-3091(2015)\n[3] FAO AQUASTAT - FAO’s Global Information System on Water and Agriculture.\nHttps://www.fao.org/aquastat/en/overview/.(2023)\n[4] ICID International Commission on Irrigation and Drainage: Sprinkler and Micro Irri-\ngatedarea.(2012)\n[5] Rohwer, J., GertenW, D. & Lucht Development of functional irrigation types for im-\nprovedglobalcropmodelling.(PotsdamInstituteforClimateImpactResearch,2007)\n[6] Skaggs,R.&Samani,Z.Farmsize,irrigationpractices,andon-farmirrigationefficiency.\nIrrigation And Drainage: The Journal Of The International Commission On Irrigation And\nDrainage.54,43-57(2005)\n[7] Tai,X.,Feng,F.&Sun,F.Farmers’WillingnessandAdoptionofWater-SavingAgricul-\ntureinAridAreas: EvidencefromChina.Sustainability.16,8112(2024)\n[8] Danabasoglu, G., Lamarque, J., Bacmeister, J., Bailey, D., DuVivier, A., Edwards, J.,\nEmmons, L., Fasullo, J., Garcia, R., Gettelman, A. & Others The community earth\nsystem model version 2 (CESM2). Journal Of Advances In Modeling Earth Systems. 12,\ne2019MS001916(2020)\n[9] Yao, Y., Vanderkelen, I., Lombardozzi, D., Swenson, S., Lawrence, D., Jägermeyr, J.,\nGrant, L. & Thiery, W. Implementation and evaluation of irrigation techniques in the\ncommunitylandmodel.(FortCollins,Colo.:[Verlagnichtermittelbar],2022)"}]

Decision letter