Impacts of irrigation expansion on moist-heat stress based on IRRMIP results

Domain: earth_science
Published: 2025-01-26
Editorial outcome: major_revision

Abstract
Irrigation rapidly expanded during the 20th century, affecting climate via water, energy, and biogeochemical changes. Previous assessments of these effects predominantly relied on a single Earth System Model, and therefore suffered from structural model uncertainties. Here we quantify the impacts of historical irrigation expansion on climate by analysing simulation results from six Earth system models participating in the Irrigation Model Intercomparison Project (IRRMIP). Results show that irrigation expansion causes a rapid increase in irrigation water withdrawal, which leads to less frequent 2-meter air temperature heat extremes across heavily irrigated areas (≥4 times less likely). However, due to the irrigation-induced increase in air humidity, the cooling effect of irrigation expansion on moist-heat stress is less pronounced or even reversed, depending on the heat stress metric. In summary, this study indicates that irrigation deployment is not an efficient adaptation measure to escalating human heat stress under climate change, calling for carefully dealing with the increased exposure of local people to moist-heat stress.

Introduction
Irrigation increases crop yield and currently accounts for more than 70% of total human freshwater use1. Irrigation-related water withdrawal and application have substantial impacts on global and regional water, energy, and biogeochemical cycles2–5, and therefore can change the magnitude and pattern of some meteorological conditions6–8. Notably, irrigation has a cooling effect on near-surface temperature9,10, especially during hot extremes11,12. For that reason, irrigation has been proposed as a potential land management strategy for balancing extreme heat escalation under anthropogenic climate change12–14.

Most previous studies only focused on temperature differences, ignoring that human comfort is also affected by heat dissipation15. Evaporative cooling is one of the main ways humans lose heat16, and air humidity and wind speed greatly influence evaporation efficiency17. These metrics can also be altered by irrigation18,19, suggesting that irrigation-induced impacts on human comfort during heat stress may be complex. Multiple moist-heat metrics were developed to quantify the compound effects of different meteorological conditions on heat stress20,21, and some of them have been used in irrigation-related studies. For example, over intensely irrigated regions in India, the wet-bulb temperature (Tw) was simulated to increase due to irrigation, despite a lowering of the temperature22,23. In addition to the ignorance of changes in air humidity, existing modelling studies generally use a static irrigated land map and rely on a single Earth System Model (ESM), which can introduce uncertainties in their results.

To address these limitations, we launched the Irrigation Model Intercomparison Project (IRRMIP) to comprehensively explore the impacts of irrigation expansion on climate and water resources during the 20th century. The IRRMIP protocol consists of two transient AMIP-style24 historical climate experiments, one with and one without irrigation expansion, during the period 1901–2014. Here we analyse IRRMIP simulations from six ESMs to study the impacts of irrigation expansion on historical (moist-)heat stress. We calculate several moist-heat metrics based on 3-hourly 2-m air temperature (T2m), 2-m air relative humidity, and 10-m wind speed. By comparing the results from different experiments and periods, we separate the effects of irrigation expansion and other forcings on high percentiles of these metrics. We aim at consolidating the understanding of irrigation-induced impacts on (moist-)heat stress, facilitating the inclusion of these impacts in future local land-use and land-management planning.

Results
Irrigation expansion drives the increase in water withdrawal
Global area equipped for irrigation has experienced substantial expansion from 1901 to 2014, increasing almost sixfold from 0.5×106 km2 to around 3×106 km2 (Fig. 1a). Expansion mainly happened in some irrigation hotspots, including South Asia (SAS), East Asia (EAS), and Central North America (CNA) (Fig. 2a). In 1901, most of the global irrigated land was concentrated over the regions where rice is the main staple food, such as India, China, and Japan (Fig. S3a). Since 1901, irrigated land has risen slowly over many regions until the 1950s, followed by an accelerated increase during the second half of the century (Fig. 1a). Until 2014, the irrigated areas in India and China experienced intensification and expansion, with some grid cells having a ≥40% (of the grid cell area) increase in irrigated area (Fig. 2a, b). Over North America, some new densely irrigated grid cells appear in CNA, while for other regions, the expansion is limited (mostly below 10% of the grid cell area).

The spatial pattern of simulated irrigation water withdrawal (IWW) is in agreement with the distribution of the area equipped for irrigation (Fig. 2c, d). India is still the most intensively irrigated region, consisting of many grid cells (0.9°×1.25°) with more than 250 mm yr−1 of IWW. The six IRRMIP models simulate a broad range of global IWW (900–4000 km3 yr−1 after the year 2000), but the temporal trend of simulations with transient irrigation extent (hereafter referred to as tranirr) generally aligns with that of the area equipped for irrigation. Consistent with a previous review5, simulated annual IWW during the post-2000 period ranges from ~900 to ~4000 km3 yr−1 (Fig. 1c–h), due to different representations of irrigation in these models. CESM2, CESM2_gw, and NorESM2 show high similarity because they share the same atmosphere and land models, with comparably small differences in specific sub-models (see Supplementary Note 1).

Based on IWW and irrigated area, we select several regions, including West North America (WNA), CNA, North Central America (NCA), Mediterranean (MED), West Central Asia (WCA), SAS, EAS, and Southeast Asia (SEA) (Fig. 2a), to calculate regional irrigation water quantities (Fig. 1c–h). SAS is the region with the highest IWW in all models, but its relative importance varies. For example, after the year 2000, SAS accounts for around one-third of global IWW simulated by CESM2 (32.4–35.8%), CESM2_gw (31.4–35.1%), E3SM (27.5–41.8%), and NorESM (32.8–35.1%), but in IPSL-CM6, this fraction is less than one-fourth (16.0–20.4%). WCA consumes the second-highest quantity of IWW, even though its area equipped for irrigation is less than EAS. This indicates that simulated IWW is dependent not only on the area equipped for irrigation but also on other factors, such as background climate conditions.

Different feedback from heat and moist-heat stress to irrigation expansion
Here we focus on T2m, HUMIDEX (HU: Eq. (2)), and Tw (Eq. (4)) warm extremes, as many other metrics are weighted average values of T2m and Tw. Changes in high percentiles of these three metrics could be interpreted as the impacts of irrigation expansion and other forcings on dry heat stress (T2m), human comfort (HU), and humid heat stress (Tw) (Table 1). In the tranirr experiment, climate change, land use change, and irrigation expansion, are all transient throughout the simulation period, and in the 1901irr experiment, the only difference is that irrigation extent is fixed at the level in year 1901 (simulation protocol of IRRMIP can be found in Supplementary Note 2).

By comparing the last-30-year of tranirr (tranirr(1985–2014)) to the first-30-year of 1901irr (1901irr(1901–1930)), we can quantify the impacts of all forcings (greenhouse gas emissions, land use and land management change, irrigation expansion, etc.). Similarly, the difference between the first- and the last-30-year periods of 1901irr (1901irr(1901–1930) and 1901irr(1985–2014)) gives the impacts of all forcings minus irrigation expansion. Finally, by subtracting the outputs of tranirr(1985–2014) from 1901irr(1985–2014), we obtain the impacts of irrigation expansion.

We first calculate the 99.9th percentile values (one-in-1000 time steps warm event) of three metrics in three exp_periods (1901irr(1901–1930), 1901irr(1985–2014) and tranirr(1985–2014)), and take the one of 1901irr(1901–1930) as the reference (Fig. 3a–c). Several irrigation hotspots, like SAS, WCA, NCA, and CNA, are also extreme heat hotspots, with a 99.9th percentile value of T2m exceeding 40 °C in many grid cells. Other forcings cause a general warming signal (+0.5 to +2 °C, see Fig. 3g), except in some areas over SAS and EAS, which may be attributed to the increasing aerosol concentrations25,26. Irrigation expansion has substantial cooling impacts in heavily irrigated grid cells (>1 °C) and weaker impacts over surrounding grid cells (<0.5 °C) (Fig. 3j). This cooling effect creates regional irrigation-induced ‘cooling islands’ against the global warming background, like the Indo-Gangetic Plain and several grid cells in Central USA (Fig. 3d). Irrigation expansion’s cooling impacts on the extreme value of HU are much less substantial in magnitudes (Fig. 3k), as a result of the increased humidity, and the ‘cooling islands’ do not exist if HU is picked as the indicator (Fig. 3e). Interestingly, Tw extreme value is only affected by irrigation expansion in limited regions, like several pixels in WCA, CNA, and the Mediterranean, but over other traditional hotspot regions like SAS and EAS, the impacts are negligible (Fig. 3l).

To quantify the changes in extreme heat events frequency, we then calculate the probability ratio (PR: Eq. (5) and Table 1) of T2m, HU, and Tw warm extremes between different exp_periods (Fig. 4, S4–6). We find that the pattern of these impacts is similar to those on the absolute value of heat extreme events (Fig. 3). Other forcings, especially greenhouse gas emissions, contribute to a warmer world, with >4 times increased frequency for the events of all three metrics in most grid cells in tropical and sub-tropical regions (Figs. S4f, S5f, and S6f).

Most models agree that irrigation expansion substantially reduces the frequency of T2m extremes, especially in SAS, WCA, and CNA (Fig. 4a, d, g and S4i). However, its impacts on HU and Tw are much less pronounced and are also less consistent among models (Fig. 4b, c, e, f, h, i, S5i and S6i). Similarly, for Tw, the dampening impacts of irrigation on extreme high events disappear over the most intensely irrigated area like India and are reversed to an intensifying effect in the Central CNA and WCA, where the 99.9th percentile Tw event happens ≥2 times more often due to irrigation expansion (Fig. 4c, f).

The more extreme the T2m heat events are, the more pronounced irrigation expansion-induced impacts become (Fig. S4). The impacts of irrigation expansion on T2m are mainly limited to irrigation hotspots for the 99th percentile heat event, e.g., large changes in PR (that is, >2 times less likely) are only found in North India and Central USA (Fig. S4g). When the events get more extreme (99.5th percentile and 99.9th percentile), the affected areas expand around these hotspots and also appear in other regions like Europe and East China (Fig. S4h, i). The slight cooling impacts on HU are also more pronounced when events get more extreme (Fig. S5), and the warming effects on Tw do not change substantially (Fig. S6). We also calculate irrigation expansion’s impacts on other moist-heat metrics (Fig. S7, description of metrics can be found in Supplementary Note 3). We find that the significance of irrigation expansion’s impacts on the apparent temperature is similar to HU, and as for other moist-heat metrics, the magnitude and consistency depend on the weight of Tw and T2m.

We calculate the average annual hours (weighted by areas) exposed to extreme events of the grid cells with more than 40% (of the grid area) irrigation expansion (Fig. 5). The average annual hours exposed to T2m extreme events show a slight increase in the first half of the century, then increases rapidly between 1950 and 1980, and keeps steady in the last decades. Irrigation expansion causes a decreasing trend in the hours exposed to heat extremes in heavily irrigated regions, in spite of global warming. However, for HU, the cooling impacts of irrigation expansion are quite small, thus the hours exposed to extreme HU events still increase, but at a slightly slower speed than those without irrigation expansion. Interestingly, the hours exposed to Tw extreme events for both 1901irr and tranirr remain almost unchanged until 1980. After 1980, this starts to rise rapidly. In the case of tranirr, the hours are slightly higher than 1901irr, indicating the intensifying impact of irrigation expansion over these grid cells. Since T2m extremes are the most substantially affected events, we also calculate the average annual hours exposed to different magnitude of T2m extreme events (the 99.0th, the 99.5th and the 99.9th percentile events) over different groups of grid cells (with 0–20%, 20–30%, 30–40%, and more than 40% of irrigation expansion) (Fig. S8). Despite the difference in magnitudes, irrigation expansion has similar reducing impacts on all degrees of T2m extreme events, which become more substantial with the extent of irrigation expansion.

Irrigation expansion-induced impacts on energy fluxes
T2m generally has positive correlation with the land surface temperature and sensible heat flux (SHF)27, and land surface temperature directly determines the upwelling longwave radiation flux (LWup). Results show that both LWup (Fig. 6a) and SHF (Fig. 6b) decrease due to irrigation expansion, which explains its cooling impacts. Based on surface energy balance, the sum of SHF and LWup could be calculated as Eq. (1):

LWup + SHF = SWdown − SWup + LWdown − LHF + R (1)

where SWdown and SWup denote the down/upwelling shortwave radiation, LWdown is the downwelling longwave radiation, LHF is the latent heat flux, and R means the residual term which includes the heat storage and fluxes in ground, water, vegetation, as well as anthropogenic heat sources and sinks.

It is known that irrigation practices substantially increase LHF due to enhanced evapotranspiration7,10,28 (Fig. 6c), but its impacts on other radiative fluxes are less pronounced (Fig. 6d–f), which align with previous studies10,28,29. In general, increase in LHF is the main reason for irrigation’s cooling impacts, which, however, also is the origin of negative impacts of higher humidity. Some other measures could be considered to provide additional cooling while avoiding the moistening effects, like enlarging the albedo (increasing SWup)13, and the most importantly, reducing greenhouse gas concentrations (decreasing LWdown). In addition, switching from low-efficiency irrigation to high-efficiency irrigation may also help reducing exposure to moist-heat stress30.

Methods
Participating ESMs and simulation protocol
Six combinations of state-of-art Earth system models (ESMs) and irrigation parametrizations are used in this study: the Community Earth System Model version 2 (CESM2)49, CESM2 with groundwater withdrawal and flow representation (CESM2_gw)?50, the Institut Pierre-Simon Laplace Climate Model version 6 (IPSL-CM6)51 with a newly developed irrigation scheme36, the Norwegian Earth System Model version 2 (NorESM2)52, the Energy Exascale Earth System Model Version 2 (E3SMv2)53 with active two-way coupled irrigation scheme33, and the Centre National de Recherches Météorologiques Climate Model version 6 (CNRM-CM6-1)54,55. Irrigation is represented in different ways in these models, which can be divided into two categories: soil-moisture-based schemes and external forcing applications (only for CNRM-CM6-1). Differences between soil-moisture-based irrigation modules relate to irrigation triggers, start time, duration, amount, and the method of water application. The employed ESMs provide the option to customise irrigation-related parameters, but in this study, all ESMs used default parameter values. CESM2, CESM2_gw, and NorESM2, share identical or similar land system models, which explains why they show strong consistency between each other. However, their differences in atmospheric models and features in the irrigation scheme, still represent an added value to this study. More detailed description of ESMs and their irrigation representations could be found in Supplementary Note 1.

We design two historical experiments in this study: with (tranirr) and without (1901irr) historical irrigation expansion. The simulations of both experiments follow the protocol of AMIP simulations in CMIP6 with the same input data56, which means that the ocean model is switched off and sea surface temperatures are prescribed. To better capture the signal of irrigation extent increase in the 20th century, we select a simulation period of 1901–2014. The only difference between the two experiments is that in tranirr, irrigation extent is transient, while in 1901irr, the irrigation extent is fixed at the level in the year 1901. One exception is IPSL-CM6, where irrigation is entirely switched off for 1901irr, and we include outputs of this ESM in the analysis as in the year 1901, irrigated fraction is very limited (Fig. S3a). The land-use map and time-series data used in these simulations are from the Land-Use Harmonization phase 2 project (LUH257), in which all crop management activities-related data, including irrigated land distribution, is obtained from the History Database of the Global Environment 3.2 (HYDE3.2)58. The irrigated land data from HYDE3.2 during the period 1901–2014 has multiple sources: for the period pre-1960, the data is collected directly from the global historical irrigation dataset (HID)59, and for the post-1960 period, the numbers are calculated by multiplying the term “area equipped for irrigation" in FAO statistics60 by the fraction of actual irrigated area in area equipped for irrigation from HID.

Climate extremes
Most models report output variables at the 3-hourly frequency to enable analysis of sub-daily extremes, but CNRM-CM6-1 only provides daily mean, maximum, and minimum values, so we calculate several moist-heat metrics based on daily maximum temperature and minimum air relative humidity to calculate the maximum metrics consistent with a previous study61, thereby assuming that the lowest relative humidity occurs when the temperature is maximum.

HUMIDEX (HU) is a feel-like heat stress metric developed in the late 1970s, and it was first used for the meteorological service in Canada62. It is calculated as Eq. (2):

HU = TC + 5/9 * (eRH * 10 - 10) (2)

where TC is the temperature at 2-meter height (°C), eRH (Pa) is the vapour pressure calculated based on relative humidity (RH) and saturated vapour pressure (es in Pa), as shown in Eq. (3):

eRH = RH * es (3)

HU is still used by the Canadian Centre for Occupational Health and Safety (CCOHS) to inform the general public if the weather conditions may be comfortable based on the following thresholds: 20–29, 30–39, 40–45, and over 46 represent the warning of ’little discomfort’, ’some discomfort’, ’great discomfort’, and ’dangerous’, respectively63.

Tw is a measure of heat stress considering the maximum potential evaporative cooling impact64. It can be measured by a thermometer covered in water-soaked cloth over which air is passed65. The calculation of Tw is very computationally expensive, so we employ a simplified method66, as indicated in Eq. (4):

Tw = TC * arctan(0.151977 * (RH + 8.313659)^0.5) + arctan(TC + RH) - arctan(RH - 1.676331) + 0.00391838 * (RH)^1.5 * arctan(0.023101 * RH) - 4.68035 (4)

Based on previous studies64,67, when Tw is over 31 °C, physical labour becomes impossible, and exposure to Tw exceeding 35 °C for more than 6 h is dangerous even for healthy individuals. However, the maximum evaporative cooling (swamp cooler) is not easy to approach, as in previous heat wave events, high casualties already existed even if the Tw is less than 28 °C68. Other metrics described in Supplementary Note 3 are also calculated based on simulations by all six models, except that we do not calculate the apparent temperature from CNRM-CM6-1 outputs, given the lack of the appropriate wind speed variable.

Data processing
Outputs from E3SM, IPSL-CM6, and CNRM-CM6-1 are firstly regridded to the resolution of CESM2, CESM_gw, and NorESM (0.9°×1.25°) (original resolution can be found in Supplementary Note 2), and then the moist-heat metrics are calculated (Eqs. (2) and (4)). After calculating the moist-heat metrics, we further processed the results to separate the impacts of irrigation expansion from other forcings. We first calculated irrigation expansion’s impacts on the absolute values of extreme events as well as surface energy fluxes. We select two periods, the first 30 years (1901–1930) and the last 30 years (1985-2014), for both simulations (tranirr and 1901irr) to calculate the impacts of all forcings, all forcings except irrigation expansion, and irrigation expansion, on near-surface climate (see Table 1). We assume that the difference between the results during 1901–1930 of 1901irr and those during 1985–2014 of tranirr represent the consequence of all forcings. The difference between the results during the two periods for the 1901irr simulations is assumed to be the consequence of other forcings. The difference between these two simulations during the 1985–2014 period is assumed to represent the impacts of irrigation expansion as the only difference between them is whether irrigation extent is transient.

Apart from the absolute value of extreme events, we also calculate the probability ratio (PR) for several extreme events, which has been used in previous studies to show the changes in climate extreme events frequency12,69. The extreme events defined in this study are based on percentile values, e.g., a 99th percentile event means that in the reference period, it happens once per 100 time steps (3 h per 300 h for 3-hourly outputs), or in other words, it indicates the 1% time steps with the most extreme values. Considering that outputs from CNRM-CM6-1 is at a daily frequency, we select the 92nd, 96th, and 99.2nd percentile events for this ESMs to represent the 99th, 99.5th, and 99.9th percentile events simulated by other ESMs. This is based on an assumption that those extreme events calculated based on maximum temperature and minimum humidity last 3 h during the day, so a 92nd percentile value means 8 days per 100 days and then 24 h per 2400 h, which is equal to 3 h per 300 h for 3-hourly outputs. The PR is calculated as:

PR = Pnew(Xext) / Pref(Xext) (5)

where Pnew(Xext) is the probability of a certain kind of extreme event (Xext) during the new exp_period and Pref(Xext) is the probability of the same kind of event during the reference exp_period. Let us assume, for example, that the 99th percentile value of T2m is 33 °C in the reference exp_period, meaning the extreme events are the ones with T2m > 33 °C: if the frequency of these events is 4% in the new exp_period, then the PR is 4. Thus, a PR of more than 1 indicates that this event occurs more frequently in the new exp_period compared to the reference exp_period, and vice versa. Similar to absolute values, we also select two periods (1901–1930 and 1985–2014) for both tranirr and 1901irr, then calculate the PR between them for the 99.0th, 99.5th, and 99.9th percentile events for T2m, HU, and Tw. To further investigate the temporal trend of irrigation expansion’s impacts, we calculated the mean value of annual hours exposed to warm extremes for four groups of grid cells, with irrigation expansion of 0–20%, 20–30%, 30–40%, and more than 40%.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This is an interesting article demonstrating that the cooling effect of additional evapotranspiration created by irrigated agriculture also resulted in increased humidity so that the overall effect on the health of the population living in the neighbourhood of the irrigated fields and exposed to the moist-heat stress is less pronounced than when considering the cooling effect alone. Based on an ensemble of earth system – and climate models the authors estimate the impact of the irrigation expansion within the last century on the severity and frequency of heat waves and moist-heat extremes. The innovation is certainly the combination of distinct model estimates and the use of a large number of heat stress indicators. This allows to derive information about the uncertainty of simulation results. Interestingly, while the models disagree on the specific impact of irrigation expansion, they agree in general trends so that it is possible to generalize findings. This is extremely relevant.\nThe manuscript is well written and the results are nicely described in a number of high-quality tables and figures. I appreciate in particular the description of the models and of their setup in contained in the Supplementary Information. In general, I highly recommend to publish the manuscript in Nature Communications but have a few comments that might be considered in a revised version of the manuscript:\nGeneral comments:\n1) The authors use a variety of Earth System – and Climate Models and compute the impact of irrigation expansion on a number of heat stress indices and if I understood it right, they also used additional heat stress indices described in Supplementary Note 3. Results are shown for the three indices Air temperature in 2 m height, HUMIDEX and wet-bulb-temperature while often the median of the model results is shown. I would appreciate when also information for distinct models will be provided similar to irrigation water withdrawal shown in Fig. 1. Maybe at least the heat indicators computed with the models simulating the highest and the lowest irrigation water withdrawal would be very helpful. If the heat stress was also computed for the indicators described in Supplementary Note 3, then I would highly appreciate when the authors make these results available as well. This could be done in a simple qualitative way in a summary table by showing the model as columns and the heat indicators as rows and then reporting whether irrigation expansion resulted in strong increase, moderate increase, no clear trend, moderate decrease or strong decrease.\n2) To avoid confusion of the readers I suggest that the authors make very clear that they analysed specifically heat stress felt by humans while not looking into heat effects on irrigated plants. There is no doubt that plants receiving irrigation are less affected by heat stress, simply because of the cooling effect of ET on the canopy and foliar temperature. These positive impacts also appear when humidity is increased. What the authors studied are the indirect effects of irrigation on people living and working in the surrounding of irrigated fields.\nSpecific comments:\n1) Please number the equations in the manuscript and refer then in the text to these numbers where appropriate.\n2) Lines 332-336: So far I know FAOSTAT is providing data in general only for the period since 1961 so that I’m surprised that HYDE3.2 obtained the irrigation information for the pre-1961 period directly from FAOSTAT. Please check.", "reviewer_2": "General Comments:\nThis study investigated the influence of irrigated cropland expansion on moist heat stress. With the latest results of six ESMs from a model comparison project, the authors examined the impacts of irrigation on human-perceived heat stress calculated with multiple different heat metrics. The relevant topic has been popular in recent years under the escalating heat stress worldwide, and the results of this study provide more solid evidence for such issues. The manuscript is generally well-written, most of the results are clearly presented, and it is suitable to be considered for publication in Nature Communications. Nonetheless, major revision is still needed to show more results for humidity changes and discuss why the results are potentially different from some existing studies. I list my specific comments as follows:\nMajor comments:\n1. The impact of irrigation on moist heat stress has been a popular topic in recent years, and many studies have been done by using either global scale simulations or regional scale simulations. Most of these studies suggest that the irrigation in the heavily irrigated regions (e.g., northern India, North China Plain) tends to increase (either large or small) the extreme value of Tw in these regions, which is found for both regional and global simulations. However, in the results of this study, such enhanced Tw extremes are not observed for these regions (Figures 3 and 4). How can we understand this discrepancy, and what could be the potential reasons? Such a discussion and comparison are necessary to avoid confusing the community. I listed some existing studies as follows for the authors to check:\nRegional simulations:\n• Kang and Eltahir, North China Plain threatened by deadly heatwaves due to climate change and irrigation, Nature Communications, 2018.\n• Mishra et al., Moist heat stress extremes in India enhanced by irrigation, Nature Geoscience, 2020.\n• Jha et al., Limited influence of irrigation on pre-monsoon heat stress in the Indo-Gangetic Plain, 2022.\nGlobal simulations:\n• Krakauer et al., Effect of irrigation on humid heat extremes, ERL, 2020.\n2. Moist heat stress or humid heat receives more attention as it could be closer to the heat stress the human body feels. In this regard, where the population gathers (i.e., urban area) should be paid greater attention, as it has more exposure (population scale) and hazard (urban heat island). For example, Guo et al. (2022) investigated the effects of irrigated cropland expansion on urban moist heat in northern India. They found cities with more considerable irrigated cropland expansion in the surrounding area could have higher Tw increase in recent decades. Currently, most of the ESMs may be unable to adequately represent the urban climate and urban heat island reasonably due to the lack of urban modules and the coarse spatial resolution. I think this also holds true for the current study; thus, the lack of urban representation in the simulation settings should be admitted as a limitation in the discussion part.\n• Guo et al., Irrigated cropland expansion exacerbates the urban moist heat stress in northern India, ERL, 2022\n3. The current manuscript shows the results based on air temperature and several heat metrics (e.g., Tw). To present the results more straightforwardly and facilitate the compassion with previous studies, I suggest the authors also provide the maps for the mean and extreme values of relative humidity and specific humidity (could be in the appendix), which is more straightforward for the readers to understand the changes of several heat metrics.\n4. Compared to the previous studies, one of the strengths of the current study is the calculation of multiple heat metrics with different algorithms, and it reveals the uncertainty when choosing different heat metrics to quantify the impacts of irrigation. The uncertainty among different heat metrics and the selection of heat metrics in specific locations for application (e.g., heat health alert and climate change impact assessment) is also a big concern for environmental health scientists and physiologists. To highlight this issue, increase the readership of current studies and facilitate communications between researchers in different fields. The authors could add more discussion on this point by referring to some environmental health studies. Some references are provided as follows:\n• Lo et al., Optimal heat stress metric for modelling heat-related mortality varies from country to country, International Journal of Climatology, 2023.\n• Guo et al., Regional variation in the role of humidity on city-level heat-related mortality, PNAS Nexus, 2024.\nMinor comments:\n1. In line 70, the sentence “Evaporative cooling is the main way humans lose heat” is too strong. Evaporative cooling only works when the human body starts to sweat, and there are also other mechanisms for the human body to lose heat, such as convection heat exchange and radiation heat exchange.\n2. Line 168, for “Consistent with a previous study using the Goddard Institute for Space Studies (GISS) climate model”, please give the reference.\n3. Figure A8 is referred to in the main text before A6 and A7. Maybe their sequence number should be adjusted.\n4. Line 249, a strange “A5” here.\n5. Lines 337-342, is the calculation based on daily maximum temperature and minimum RH only applied for CNRM-CM6-1, or is it applied for all six ESMs? It should be made more clear here. Additionally, what wind speed value (daily mean or maximum?) is used when calculating APT using daily maximum temperature and minimum RH?\n6. There is no e in Figure A1, as the figure caption states.\n7. In the caption of Figure 1, I assume the c-h represents the IWW for the tranirr simulations. It should be specified. In addition, in panel b of Figure 1, the median of the 1901irr simulations (red color) is so close to the minimum value (the lower boundary). What is the reason? Moreover, according to the caption “for the 1901irr experiment of IPSL-CM6, irrigation is switched off”, if the irrigation is switched off for IPSL-CM6, then why the lower boundary of the red color in panel b is not 0?"}, "author_response": "4 NatureCommunications–Responsetoreviewers\n1.1 Majorcomments\nReviewer1Comment1\nThisisaninterestingarticledemonstratingthatthecoolingeffectofadditionalevap-\notranspirationcreatedbyirrigatedagriculturealsoresultedinincreasedhumidityso\nthat the overall effect on the health of the population living in the neighbourhood of\ntheirrigatedfieldsandexposedtothemoist-heatstressislesspronouncedthanwhen\nconsideringthecoolingeffectalone. Basedonanensembleofearthsystem–andcli-\nmate models the authors estimate the impact of the irrigation expansion within the\nlast century on the severity and frequency of heat waves and moist-heat extremes.\nThe innovation is certainly the combination of distinct model estimates and the use\nof a large number of heat stress indicators. This allows to derive information about\nthe uncertainty of simulation results. Interestingly, while the models disagree on the\nspecificimpactofirrigationexpansion,theyagreeingeneraltrendssothatitispossi-\nbletogeneralizefindings. Thisisextremelyrelevant. Themanuscriptiswellwritten\nand the results are nicely described in a number of high-quality tables and figures. I\nappreciateinparticularthedescriptionofthemodelsandoftheirsetupincontained\nin the Supplementary Information. In general, I highly recommend to publish the\nmanuscriptinNatureCommunicationsbuthaveafew commentsthatmightbecon-\nsideredinarevisedversionofthemanuscript.\nResponse We thank Reviewer 1 for these kind words and the valuable feedback that we\nused to improve our manuscript. Below, we address every comment carefully and explain\nthecorrespondingchangesinthemanuscript. Thereareseveralmajorchangeswehavedone\\inthemanuscript,whicharelistedasfollows.\n(1) Previously, we compared the outputs during the two periods (1901-1930 and 1985-2014)\nof tranirr to calculate the impacts of all forcings. This was based on an assumption that\nthedifferenceinoutputsduringtheperiod1901-1930betweentwoexperiments(tranirrand\n1901irr) is negligible. However, there was already irrigation expansion happening during\nthefirst30years,andwethereforechangedthemethodofcalculatingtheimpactsofallforc-\nings. Table1inthemaintextthenhasbeenmodifiedasinTAB1.\n(2)Wehaverewrittensection’Irrigationexpansion-inducedimpactsonenergyfluxes’toim-\nprovetheflowofthemanuscriptsandhighlighttheimportantchangesmoreclearly.\n(3)Before,infigureswithagreementamongmodels,onlyresultswithhighagreementwere\nshown. Wenowshowallresultsandaddhatchestoindicatetheagreement.\n(4)AppendixfigureshavebeenmovedtoSupplementaryInformation.\n1. Reviewer1 5\nTAB1: Approachtoseparatetheimpactsofdifferentforcings\nReferenceexp_period Newexp_period Forcings\nallforcings(greenhousegasemissions,\n1901irr_1901-1930 tranirr_1985-2014 landusechange,\nirrigationexpansion,etc.)\n1901irr_1901-1930 1901irr_1985-2014 allforcingsexceptirrigationexpansion\n1901irr_1985-2014 tranirr_1985-2014 irrigationexpansion\nReviewer1Comment2\nTheauthorsuseavarietyofEarthSystem–andClimateModelsandcomputetheim-\npact of irrigation expansion on a number of heat stress indices and if I understood it\nright, they also used additional heat stress indices described in Supplementary Note\n3. Results are shown for the three indices Air temperature in 2 m height, HUMIDEX\nand wet-bulb-temperature while often the median of the model results is shown. I\nwouldappreciatewhenalsoinformationfordistinctmodelswillbeprovidedsimilar\nto irrigation water withdrawal shown in Fig. 1. Maybe at least the heat indicators\ncomputed with the models simulating the highest and the lowest irrigation water\nwithdrawalwouldbeveryhelpful. Iftheheatstresswasalsocomputedfortheindi-\ncators described in Supplementary Note 3, then I would highly appreciate when the\nauthors make these results available as well. This could be done in a simple qualita-\ntivewayinasummarytablebyshowingthemodelascolumnsandtheheatindicators\nas rows and then reporting whether irrigation expansion resulted in strong increase,\nmoderateincrease,nocleartrend,moderatedecreaseorstrongdecrease.\nResponse We agree with Reviewer 1 that showing results of other models and other heat\nmetricsgreatlyaddsvaluetothismanuscript,soweaddedtwosectionsintheSupplemen-\ntary Materials (Supplementary Figures 1 and 2). Supplementary Figures 1 contain maps\nof irrigation water withdrawal (Figure S5), maps of impacts on absolute values of 2-meter\nairtemperature,HUMIDEX,andwet-bulbtemperatureextremeevents(FigureS6-S11),and\nmapsofimpactsonfrequencyof2-meterairtemperature,HUMIDEX,andwet-bulbtemper-\nature extreme events (Figure S12-S17) of six individual models. We now also refer to these\nsections in the main text wherever necessary. For example, in the caption of Figures 2 and\n3:\nSpatialpatternofirrigationexpansionandsimulatedirrigationwaterwith-\ndrawalindifferentperiods. [...]. Thefigureshowingresultsfromallindividual\nmodelscanbefoundinSupplementaryFigures1. Multi-modelmeansimulated\nannualIWWwithfixedirrigationextent(1901irr)duringthetwoperiodscanbe\nfoundinFigureA1e,f.\nHeat and moist-heat extremes and impacts of different forcings on them.\n[...] d-l Multi-model mean impacts of all forcings (ALL: greenhouse gas emis-\nsions, land use and land management change, irrigation expansion, etc.) (d,e,f),\nallforcingsexceptirrigationexpansion(ALL-IE:g,h,i),andirrigationexpansion\n(IE: j,k,l) on the 99.9th percentile values of T (d, g, j), HU (e, h, k), and T\n2m w\n(f,i,l). Impacts are calculated by subtracting the values in the new exp_period\nby those in the reference exp_period (see Table 1). Hatches indicate that signals\n(>0.1 or <-0.1) are agreed by ≥ 5 of 6 models (results for the individual models\nare presented in Supplementary Figures S7-11). Results for other extreme event\npercentiles(the99th and99.5th)areshowninSupplementaryFiguresS18-26.\nTo illustrate the differences across individual models, we show probability ratio calculated\nbased on outputs from two models, CESM2 (FIG 1) and CNRM-CM6-1 (FIG 2). Results of\nothermodelscanbefoundinSupplementaryFigure1.\nFIG1: Impactsofdifferentforcingsonfrequencyof2-meterairtemperature,HUMIDEX,\nand wet-bulb temperature extreme event as simulated by CESM2. a-i Impacts of all forc-\nings (ALL: a, b, c), all forcings except irrigation expansion (ALL-IE: d, e, f), and irrigation\nexpansion (IE) on the frequency of the events in which 2-meter air temperature (T : a, d,\n2m\ng), HUMIDEX (HU: b, e, h), and wet bulb temperature (T : c, f, i) exceed their 99.9th per-\nw\ncentilevaluesofthefirst30years(1901-1930)inthesimulationswithoutirrigationexpansion\n(1901irr). Impactsarequantifiedbyprobabilityratio(PR)whichiscalculatedbydividingthe\nevents frequencies in the new exp_period by those in the reference exp_period (see Table 1\nin main text), and the values are calculated based on CESM2 outputs. This figure is adopted\nfromSupplementaryFigureS6.\nAdditionally, to illustrate the difference across heat metrics, we show two metrics, the Dis-\ncomfort Index (DI; FIG 3) and the temperature when 80% of the swamp coolers are ap-\nproached (T ; FIG 4). Results of other metrics can be found in Supplementary Figure\ns80\n2.\n1. Reviewer1 7\nFIG 2: Same as FIG 1 but calculated based on CNRM-CM6-1 outputs. This figure is adopted\nfromSupplementaryFigureS11.\nFIG3: a-iImpactsofallforcings(ALL:a,b,c),allforcingsexceptirrigationexpansion(ALL-\nIE:d,e,f),andirrigationexpansion(IE)onthefrequencyoftheeventsinwhichDiscomfort\nIndex(DI)exceedits99th (a, d, g), 99.5th (b, e, h), and99.9th (c, f, i)percentilevaluesofthe\nfirst 30 years (1901-1930) in the simulations without irrigation expansion (1901irr) shown\nin Figure S22a-c. Impacts are quantified by probability ratio (PR) which is calculated by\ndividingtheeventsfrequenciesinthenewexp_periodbythoseinthereferenceexp_period\n(see Table 1 in main text), and the values are the sixth root of the product of PR calculated\nfrom the outputs of six ESMs. Only signals (>1.2 or <1/1.2) agreed by ≥ 5 of 6 models are\nshown. ThisfigureisadoptedfromSupplementaryFigureS22.\n1. Reviewer1 9\nFIG4: SameasFIG3buforthetemperaturewhen80%oftheswampcoolersareapproached\n(T ). ThisfigureisadoptedfromSupplementaryFigureS26.\ns80\nReviewer1Comment3\nToavoidconfusionofthereadersIsuggestthattheauthorsmakeveryclearthatthey\nanalysedspecificallyheatstressfeltbyhumanswhilenotlookingintoheateffectson\nirrigated plants. There is no doubt that plants receiving irrigation are less affected\nby heat stress, simply because of the cooling effect of ET on the canopy and foliar\ntemperature. These positive impacts also appear when humidity is increased. What\ntheauthorsstudiedaretheindirecteffectsofirrigationonpeoplelivingandworking\ninthesurroundingofirrigatedfields.\nResponse\nThankReview1forthissuggestion. Weaddedsometextstoclarifyit.\nL294-296\nNote that both HU and T are designed for human beings, so the results\nw\nhere are not suitable for detecting irrigation-induced impacts on crops and nat-\nural plants, which requires better understanding of interactions between plant\ngrowth,temperature,andhumidity.\n1.2 Minorcomments\nReviewer1Comment4\nPlease number the equations in the manuscript and refer then in the text to these\nnumberswhereappropriate.\nResponseWethanktheReviewerforthissuggestion. Wehavelabeledalltheequationsand\nrefertothemintherevisedmanuscriptwherevernecessary. Forexample,\nL129-131:\nHere we focus on T , HUMIDEX (HU: Equation 2), and T (Equation 4)\n2m w\nwarm extremes, as many other metrics are weighted average values of T and\n2m\nT .\nw\nReviewer1Comment5\nLines332-336: SofarIknowFAOSTATisprovidingdataingeneralonlyfortheperiod\nsince1961sothatI’msurprisedthatHYDE3.2obtainedtheirrigationinformationfor\nthepre-1961perioddirectlyfromFAOSTAT.Pleasecheck.\nResponse Thanks for finding this mistake. Actually the data during the pre-1961 period is\nfromSiebertetal. (2015). Weupdatedthemanuscript:\nL369-373\nTheirrigatedlanddatafromHYDE3.2duringtheperiod1901-2014hasmulti-\nplesources: fortheperiodpre-1960,thedataiscollecteddirectlyfromtheglobal\n2. Reviewer2 11\nhistorical irrigation data set (HID) (1), and for the post-1960 period, the num-\nbersarecalculatedbymultiplyingtheterm\"areaequippedforirrigation\"inFAO\nstatistics(2)bythefractionofactualirrigatedareainareaequippedforirrigation\nfromHID.\n2.1 Majorcomments\nReviewer2Comment1\nThis study investigated the influence of irrigated cropland expansion on moist heat\nstress. With the latest results of six ESMs from a model comparison project, the au-\nthors examined the impacts of irrigation on human-perceived heat stress calculated\nwith multiple different heat metrics. The relevant topic has been popular in recent\nyears under the escalating heat stress worldwide, and the results of this study pro-\nvide more solid evidence for such issues. The manuscript is generally well-written,\nmost of the results are clearly presented, and it is suitable to be considered for pub-\nlication in Nature Communications. Nonetheless, major revision is still needed to\nshow more results for humidity changes and discuss why the results are potentially\ndifferentfromsomeexistingstudies.\nResponse We thank Reviewer 2 for these supportive words and the valuable suggestions\nwhichhelpustoimprovethismanuscript. Below,weaddresseverycommentcarefullyand\nexplain the corresponding changes in the manuscript. There are several major changes we\nhavedoneinthemanuscript,whicharelistedasfollows.\n(1) Previously, we compared the outputs during the two periods (1901-1930 and 1985-2014)\nof tranirr to calculate the impacts of all forcings. This was based on an assumption that\nthedifferenceinoutputsduringtheperiod1901-1930betweentwoexperiments(tranirrand\n1901irr) is negligible. However, there was already irrigation expansion happening during\nthefirst30years,andwethereforechangedthemethodofcalculatingtheimpactsofallforc-\nings. Table1inthemaintextthenhasbeenmodifiedasinTAB1.\n(2)wehaverewrittensection’Irrigationexpansion-inducedimpactsonenergyfluxes’toim-\nprovetheflowofthemanuscriptsandhighlighttheimportantchangesmoreclearly.\n(3)Before,infigureswithagreementamongmodels,onlyresultswithhighagreementwere\nshown. Wenowshowallresultsandaddhatchestoindicatetheagreement.\n(4)AppendixfigureshavebeenmovedtoSupplementaryInformation.\nReviewer2Comment2\nTheimpactofirrigationonmoistheatstresshasbeenapopulartopicinrecentyears,\nandmanystudieshavebeendonebyusingeitherglobalscalesimulationsorregional\nscalesimulations. Mostofthesestudiessuggestthattheirrigationintheheavilyirri-\ngated regions (e.g., northern India, North China Plain) tends to increase (either large\nor small) the extreme value of Tw in these regions, which is found for both regional\nand global simulations. However, in the results of this study, such enhanced Tw ex-\ntremesarenotobservedfortheseregions(Figures3and4). Howcanweunderstand\nthis discrepancy, and what could be the potential reasons? Such a discussion and\ncomparison are necessary to avoid confusing the community. I listed some existing\nstudiesasfollowsfortheauthorstocheck: Regionalsimulations: •KangandEltahir,\nNorth China Plain threatened by deadly heatwaves due to climate change and irri-\ngation, Nature Communications, 2018. • Mishra et al., Moist heat stress extremes in India\nenhancedbyirrigation,NatureGeoscience,2020. •Jhaetal.,Limitedinfluence\nofirrigationonpre-monsoonheatstressintheIndo-GangeticPlain,2022.\nGlobal simulations:\n• Krakauer et al., Effect of irrigation on humid heat extremes, ERL,2020.\nResponse\nWethankthereviewerforthissuggestion. Indeed,weconfirmthatourresultscontainsome\ndiscrepancies compared to previous studies on this topic, while other elements corroborate\nearlier findings. In the revised manuscript, we have now added text that discusses these\ndifferences,complementingthecomparisonwiththeKrakaueretal.,2020ERLstudywhich\nwasalreadypresentedintheoriginalsubmission:\nL254-276\nDifferent from the cooling impacts on temperature extremes, the impacts of\nirrigationonmoist-heatextremesarelesssubstantial. Asmentionedabove,over\ntwo intensely irrigated regions, India and East China, extreme events of T do\nw\nnot show pronounced changes in both magnitude and frequency (Figure 3 and\n4)duetoirrigationexpansion. Twopreviousstudies,aglobaloneusingtheGod-\ndardInstituteforSpaceStudies(GISS)climatemodel(4)andaregionalonebased\nonMassachusettsInstituteofTechnologyRegionalClimateModel(5),foundthat\nirrigation increases the average values T more substantially than the extreme\nw\nvaluesoverthesameregions. ItimpliesthatthemostextremeT eventsmaynot\nw\nhappenduringtheheavilyirrigatedseason. Thus,wecalculatemulti-yearmean\nmonthlyIWWduringtheperiod1985-2014oftranirrexperiment(FIG5),monthly\nmaximum T , HU, and T during the same period but of 1901irr experiment\n2m w\n(FIG 6, 7, and 8), which confirms this hypothesis. In India, high IWW appears\nin April to June, high T extremes exist in May to June, while HU peaks from\n2m\nJune to August, and T from July to August. This temporal mismatch has also\nw\nbeen argued by some researchers (6; 7), both regarding heat and moist-heat ex-\ntremes. Irrigationexpansion-inducedimpactsonannualmean, maximum, min-\nimum temperature (FIG 9) and specific humidity (FIG 10) also show that T is\nw\nmore substantially affected where both annual maximum temperature and spe-\n2. Reviewer2 13\ncifichumiditymatchtemporallytheirrigationseason,likeWCAandCNA.How-\never,iflimitingthestudyperiodtoirrigationseason,somestudiesstillfoundev-\nidence of irrigation-induced warming on T extremes in India (8; 3). Over East\nw\nChina,incontrast,themostintenseirrigationseasongenerallyoverlapswiththe\npeakseasonforT ,HU,andT ,andapreviousstudyhasattributedweakim-\n2m w\npacts of irrigation in this area to a lower intensity of AEI expansion than India\nandlocalclimaticconditions(3).\nFIG5: Multi-yearmeanmonthlyirrigationwaterwithdrawal(IWW)duringtheperiod1985-\n2014oftheexperimentwithirrigationexpansion(tranirr). Resultsshownareaveragevalues\nofallsixmodels. ThisfigureisadoptedfromSupplementaryFigureA7.\nFIG6: Multi-yearmeanmonthlymaximum2-meterairtemperature(T )duringtheperiod\n2m\n1985-2014oftheexperimentwithoutirrigationexpansion(1901irr). Resultsshownareaver-\nagevaluesofallsixmodels. ThisfigureisadoptedfromSupplementaryFigureA8.\n2. Reviewer2 15\nFIG 7: Similar to FIG 6 but for HUMIDEX (HU). This figure is adopted from Supplementary\nFigureA9.\nFIG8: SimilartoFIG6butforwet-bulbtemperature(T ). ThisfigureisadoptedfromSupple-\nw\nmentaryFigureA10.\n2. Reviewer2 17\nReviewer2Comment3\nMoistheatstressorhumidheatreceivesmoreattentionasitcouldbeclosertotheheat\nstressthehumanbodyfeels. Inthisregard,wherethepopulationgathers(i.e.,urban\narea)shouldbepaidgreaterattention,asithasmoreexposure(populationscale)and\nhazard (urban heat island). For example, Guo et al. (2022) investigated the effects of\nirrigatedcroplandexpansiononurbanmoistheatinnorthernIndia. Theyfoundcities\nwith more considerable irrigated cropland expansion in the surrounding area could\nhavehigherTwincreaseinrecentdecades. Currently,mostoftheESMsmaybeunable\ntoadequatelyrepresenttheurbanclimateandurbanheatislandreasonablyduetothe\nlack of urban modules and the coarse spatial resolution. I think this also holds true\nforthecurrentstudy;thus,thelackofurbanrepresentationinthesimulationsettings\nshouldbeadmittedasalimitationinthediscussionpart.\n• Guo et al., Irrigated cropland expansion exacerbates the urban moist heat stress in\nnorthernIndia,ERL,2022\nResponseThankyouforthissuggestion. Wehaveaddedthefollowingtext:\nL284-288\nInaddition,theimpactsofagriculturalirrigationonneighbouringnon-irrigated\nareas, especially urban areas with high population density, should be investi-\ngatedmorecomprehensively. Apreviousstudyhighlightedthehigherexposure\nofcitiessurroundedbyirrigatedcroplandinIndiatomoist-heatstress,asacom-\nbined consequence of irrigation and urban heat island effect (3). Unfortunately,\nurbanregionsarestillpoorlyresolvedincurrentcoarse-resolutionESMs.\nReviewer2Comment4\nThe current manuscript shows the results based on air temperature and several heat\nmetrics (e.g., Tw). To present the results more straightforwardly and facilitate the\ncompassion with previous studies, I suggest the authors also provide the maps for\nthe mean and extreme values of relative humidity and specific humidity (could be\nin the appendix), which is more straightforward for the readers to understand the\nchangesofseveralheatmetrics.\nResponse We agree that this additional information can be helpful for understanding the\nlimitedimpactsofirrigationexpansiononwetbulbtemperatureextremes. Wethereforedis-\nplay the impacts of irrigation expansion on annual mean, minimum, and maximum values\nof daily mean, minimum, and maximum 2-meter air temperature (FIG 9), respectively, and\ndidthesameforspecifichumidity(FIG10). Weadditionallycalculatedthemulti-yearmean\nmonthlymeanirrigationwaterwithdrawal,maximum2-meterairtemperature,HUMIDEX,\nand wet-bulb temperature, to verify the temporal overlap between them (see updated text\ninReviewer2Comment2).\nFIG 9: Impacts of irrigation expansion (IE) on the annual mean (a, b, c), annual maximum\n(d,e,f)andannualminimum(a,d,g)valuesofthedailymean(a,d,g),dailymaximum(b,\ne, h), and daily minimum (b, e, h) 2-meter air temperature (T ). This figure is adopted from\n2m\nSupplementaryFigureA11.\nFIG 10: Same as FIG 9 but for specific humidity. This figure is adopted from Supplementary\nFigureA12.\n2. Reviewer2 19\nReviewer2Comment5\nCompared to the previous studies, one of the strengths of the current study is the\ncalculation of multiple heat metrics with different algorithms, and it reveals the un-\ncertainty when choosing different heat metrics to quantify the impacts of irrigation.\nTheuncertaintyamongdifferentheatmetricsandtheselectionofheatmetricsinspe-\ncificlocationsforapplication(e.g.,heathealthalertandclimatechangeimpactassess-\nment) is also a big concern for environmental health scientists and physiologists. To\nhighlightthisissue,increasethereadershipofcurrentstudiesandfacilitatecommuni-\ncationsbetweenresearchersindifferentfields. Theauthorscouldaddmorediscussion\nonthispointbyreferringtosomeenvironmentalhealthstudies. Somereferencesare\nprovidedasfollows: •Loetal.,Optimalheatstressmetricformodellingheat-related\nmortality varies from country to country, International Journal of Climatology, 2023.\n•Guoetal.,Regionalvariationintheroleofhumidityoncity-levelheat-relatedmor-\ntality,PNASNexus,2024.\nResponseThankyouforthesuggestion,wenowaddedthefollowingdiscussion:\nL290-294\nFurthermore,previousstudieshaveassessedthesuitabilityofvariousmoist-\nheatmetricsbasedontheircorrelationwithlocalmortalitydatasets,anddiscov-\neredthattheoptimalmetricvariesamongcountriesandcities(9;10). Therange\nof moist-heat metrics presented here enables finely tailored investigation of irri-\ngation’simpactsonenvironmentalhealthatlocalscales.\n2.2 Minorcomments\nReviewer2Comment6\nInline70,thesentence“Evaporativecoolingisthemainwayhumansloseheat”istoo\nstrong. Evaporative cooling only works when the human body starts to sweat, and\ntherearealsoothermechanismsforthehumanbodytoloseheat, suchasconvection\nheatexchangeandradiationheatexchange.\nResponse\nWechangedourwordingto:\nL70\nEvaporativecoolingisoneofthemainwayshumansloseheat.\nReviewer2Comment7\nLine168,for“ConsistentwithapreviousstudyusingtheGoddardInstituteforSpace\nStudies(GISS)climatemodel”,pleasegivethereference.\nResponseWehaveaddedthereferencetoKrakaueretal. (2020ERL)(4).\nReviewer2Comment8\nFigure A8 is referred to in the main text before A6 and A7. Maybe their sequence\nnumbershouldbeadjusted.\nResponse Thank you for the suggestion. We have updated the order of these figures in the\nAppendix.\nReviewer2Comment9\nLine249,astrange“A5”here.\nResponseThisisatypo,wewantedtorefertotheFigureA5. Thisisnowcorrected:\nL183-185\nWe also calculate irrigation expansion’s impacts on other moist-heat metrics\n(FigureA5,descriptionofmetricscanbefoundinSupplementaryNote3).\nReviewer2Comment10\nLines337-342,isthecalculationbasedondailymaximumtemperatureandminimum\nRHonlyappliedforCNRM-CM6-1,orisitappliedforallsixESMs? Itshouldbemade\nmore clear here. Additionally, what wind speed value (daily mean or maximum?) is\nusedwhencalculatingAPTusingdailymaximumtemperatureandminimumRH?\nResponse Thank you for pointing this out. We added the following clarification in the\nMethodsection:\nL398-400\nOther metrics described in Supplementary Note 3 are also calculated based\non simulations by all six models, except that we do not calculate the apparent\ntemperaturefromCNRM-CM6-1outputs,giventhelackoftheappropriatewind\nspeedvariable.\nReviewer2Comment11\nThereisnoeinFigureA1,asthefigurecaptionstates.\nResponseThanksforfindingthismistake. Wehavecorrectedit.\nREFERENCES 21\nReviewer2Comment12\nIn the caption of Figure 1, I assume the c-h represents the IWW for the tranirr simu-\nlations. It should be specified. In addition, in panel b of Figure 1, the median of the\n1901irrsimulations(redcolor)issoclosetotheminimumvalue(thelowerboundary).\nWhatisthereason? Moreover,accordingtothecaption“forthe1901irrexperimentof\nIPSL-CM6, irrigation is switched off”, if the irrigation is switched off for IPSL-CM6,\nthenwhythelowerboundaryoftheredcolorinpanelbisnot0?\nResponseThanksforpointingoutthelackofclarificationhere. Wehaveupdatedthecaption\nofthisfigure.\na Global and regional time series of area equipped for irrigation (AEI) in\n1901-2014. The area equipped for irrigation data is from the Land-Use Harmo-\nnization phase 2 (LUH2) project (11), and the grid cells corresponding to IPCC\nreference regions are indicated in Figure 2a. b Simulated mean global irriga-\ntion water withdrawal (IWW) with (blue: tranirr) by all six models and without\n(red: 1901irr) irrigation expansion by five models (except IPSL-CM6). The line\nindicates the median value of six (or five) models and the range indicates the\nmaximumandminimumvalues. NotethatIWWofCNRM-CM6-1isappliedas\nan external input, which is the irrigation fluxes from a global reconstructed hy-\ndrological dataset based on simulations (12)., and for the 1901irr experiment of\nIPSL-CM6, irrigation is switched off. c-h Global and regional IWW (with irriga-\ntionexpansion)simulatedbyCESM2(c),CESM2_gw(d),NorESM(e),E3SM(f),\nCNRM-CM6-1(g)andIPSL-CM6(h).\nThe reason for the median of the 1901irr simulations (red color) is so close to the minimum value\n(thelowerboundary)andwhythelowerboundaryoftheredcolorinpanelbisnot0isthatweonly\nshowthesimulatedIWWoffivemodelshere(IPSL-CM6excluded).\nReferences\n[1] Siebert, S., Kummu, M., Porkka, M., Döll, P., Ramankutty, N. & Scanlon, B. A global\ndata set of the extent of irrigated land from 1900 to 2005. Hydrology And Earth System\nSciences.19,1521-1545(2015)\n[2] Foodandagriculturedata.(2024,3),https://www.fao.org/faostat/en/#home\n[3] Guo, Q., Zhou, X., Satoh, Y.&Oki, T.Irrigatedcroplandexpansionexacerbatestheur-\nbanmoistheatstressinnorthernIndia.EnvironmentalResearchLetters.17,054013(2022)\n[4] Krakauer, N., Cook, B. & Puma, M. Effect of irrigation on humid heat extremes. Envi-\nronmentalResearchLetters.15,094010(2020)\n[5] Kang,S.&Eltahir,E.NorthChinaPlainthreatenedbydeadlyheatwavesduetoclimate\nchangeandirrigation.NatureCommunications.9,2894(2018)\n[6] Jha,R.,Mondal,A.,Devanand,A.,Roxy,M.&Ghosh,S.Limitedinfluenceofirrigation\nonpre-monsoonheatstressintheIndo-GangeticPlain.NatureCommunications.13,4275\n(2022)\n[7] Kukal, M. Comment on “Improved Water Savings and Reduction in Moist\nHeat Stress Caused by Efficient Irrigation” by Anukesh Krishnankutty\nAmbika and Vimal Mishra. Earth’s Future. 11, e2022EF002892 (2023),\nhttps://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022EF002892,\ne2022EF0028922022EF002892\n[8] Mishra, V., Ambika, A., Asoka, A., Aadhar, S., Buzan, J., Kumar, R.&Huber, M.Moist\nheat stress extremes in India enhanced by irrigation. Nature Geoscience. 13, 722-728\n(2020)\n[9] Lo, Y., Mitchell, D., Buzan, J., Zscheischler, J., Schneider, R., Mistry, M., Kysely`, J., Lav-\nigne, É., Silva, S., Royé, D. & Others Optimal heat stress metric for modelling heat-\nrelatedmortalityvariesfromcountrytocountry.InternationalJournalOfClimatology.43,\n5553(2023)\n[10] Guo, Q., Mistry, M., Zhou, X., Zhao, G., Kino, K., Wen, B., Yoshimura, K., Satoh, Y.,\nCvijanovic,I.,Kim,Y.&OthersRegionalvariationintheroleofhumidityoncity-level\nheat-relatedmortality.PNASNexus.3,pgae290(2024)\n[11] Hurtt, G., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B., Calvin, K., Doelman, J.,\nFisk, J., Fujimori, S., Klein Goldewijk, K. & Others Harmonization of global land use\nchangeandmanagementfortheperiod850–2100(LUH2)forCMIP6.GeoscientificModel\nDevelopment.13,5425-5464(2020)\n[12] Wisser, D., Fekete, B., Vörösmarty, C. & Schumann, A. Reconstructing 20th cen-\ntury global hydrography: a contribution to the Global Terrestrial Network-Hydrology\n(GTN-H).HydrologyAndEarthSystemSciences.14,1-24(2010)"}, {"round": 2, "reviewer_comments": {"reviewer_2": "In my opinion, the questions raised by the reviewer have been satisfactorily addressed. I recommend it for publication."}, "author_response": ""}]

Decision letter