CO_2 radiative forcing induces summer cooling over India

Domain: biology
Published: 2026-03-24
Editorial outcome: minor_revision

Abstract
In response to anthropogenic forcing, the Earth’s surface generally warms as greenhouse gases trap outgoing longwave radiation. Counterintuitively, however, some regions exhibit surface cooling against this global warming background—a phenomenon known as a warming hole. Beyond the well-documented warming holes over the North Atlantic and southeastern United States, here we show that increasing atmospheric CO2 concentrations can also induce summertime cooling over India. Due to the direct radiative effect of CO2, warming of the Eurasian continent relative to surrounding oceans, low-level moisture transport and vertical motion are enhanced over India. Combined with abundant summer-monsoon moisture and the topographic blocking effects of the Himalayas and Hindu Kush Mountains, these circulation changes increase cloud cover. The resulting cloud enhancement reduces incoming solar radiation at the surface, producing the observed regional cooling. These results reveal a previously underappreciated mechanism whereby greenhouse gas forcing can paradoxically induce regional cooling through atmospheric dynamical pathways.

Introduction
Since the onset of the Industrial Revolution, global warming has emerged as the most prominent feature of climate change. The Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6) confirms that the last four decades have each been successively warmer than any decade since 18501; the global mean surface temperature in 2024 increased by about 1.5°C compared to pre-industrial levels2,3. A large body of observational evidence and model simulations consistently indicates that anthropogenic greenhouse gas emissions are the dominant driver of this warming1. However, regional manifestations of climate change are far from uniform, with considerable heterogeneity in both the magnitude and pattern of temperature responses4,5. Regional variations are particularly critical, as they determine local impacts on ecosystems, agriculture, and human health6,7. Against this backdrop, some regions have exhibited significantly weaker warming, or even surface cooling, during the global warming era—a counterintuitive phenomenon commonly referred to as the “warming hole”8–10.

The warming hole has been robustly identified in two well-studied regions: the subpolar North Atlantic and the southeastern United States. In the North Atlantic, the muted warming signal is strongly linked to ocean dynamics, particularly the weakening of the Atlantic Meridional Overturning Circulation, greenhouse gas–induced changes in ocean heat transport, reductions in anthropogenic aerosols, and associated atmosphere–ocean–atmosphere interactions9,11–16. In contrast, the southeastern United States warming hole has been attributed to a combination of factors including internal climate variability, aerosol forcing, and radiative forcing–induced sea surface temperature anomalies10,17–21. Together, these studies demonstrate that warming holes may arise from a range of mechanisms that vary across regions, but most explanations emphasize the central role of ocean circulation changes or aerosol forcing.

Recently, a warming hole feature over India was found by scientists22. As a monsoon-dominated region spanning tropical and subtropical latitudes, with a dense population and an economy heavily dependent on agriculture, India is highly vulnerable to temperature changes. The impacts of extreme heat events in recent years underscore this vulnerability: a devastating coastal heatwave in 2015 claimed around 2500 lives23; the extreme March 2022 heatwave pushed temperatures above 40°C in numerous Indian cities24; and in 2024 India recorded both its highest observed temperature and its longest continuous spell of heatwave25. These extreme heat events are closely linked to North Atlantic sea surface temperature anomalies, warming of the tropical Indian Ocean, El Niño events, and local land–atmosphere feedbacks26–28. Despite the increasing frequency and severity of such extreme heat events, long-term surface temperature trends over India feature a warming hole, with in-situ warming roughly half of the global average22. Existing hypotheses point to anthropogenic aerosols, changes in large-scale circulation, and irrigation expansion as possible drivers of this muted warming signal. However, these explanations remain debated.

Here, using multi-model ensembles of both atmosphere-only and atmosphere-ocean coupled simulations, we present evidence for a previously underappreciated mechanism: CO2 forcing itself, in the absence of ocean-associated processes, can induce a summer cooling over India (Fig. 1). This finding differs from the prevailing view that regional warming holes are necessarily tied to ocean circulation changes or aerosol forcing. Instead, it highlights the capacity of direct radiative forcing from greenhouse gases to reshape atmospheric circulation and alter the land–atmosphere energy balance in ways that produce localized cooling.

Results
CO2-induced summer cooling over India
During boreal summer, even without ocean-related processes, CO2 radiative forcing induces a distinct surface cooling over India. To assess its robustness, we analyzed two experiment sets from Coupled Model Intercomparison Project phase six (CMIP6): amip-related simulations (amip and amip-4×CO2 simulations) and the abrupt-related simulations (abrupt-4×CO2 and piControl simulations), hereafter referred to as “amip” results and “abrupt” results, respectively. Details of the method used to isolate the effects of CO2 radiative forcing are provided in Methods (see “Methods”: Quantification of CO2 radiative forcing effects). Twelve models participated in amip-related simulations and 48 in abrupt-related simulations.

Figure 1 demonstrates the response of surface air temperature to CO2 radiative forcing during local summer. When the sea surface temperature is fixed to its present-climate level and atmospheric CO2 concentration is quadrupled, most land areas exhibit strong warming in amip-related results (Fig. 1a). The global mean surface air temperature increases by 0.91–2.15 K, with enhanced warming over mid-latitude Eurasia and North America, reaching a maximum of 3.48 K in the multi-model ensemble (MME). Given the smaller number of models participating in amip-related results, we also examine the abrupt-related results (Fig. 1d, e), which display a similar pattern but a stronger amplitude, with global mean warming of 0.85–2.56 K and MME maximum up to 5.49 K over the center region of the United States.

In contrast, both ensembles reveal a robust summer cooling over India in response to CO2 radiative forcing alone. When the CO2 concentration is quadrupled, and the sea surface temperature is fixed, cooling occurs south of the Himalayas and the Sulaiman Range (Fig. 1b). The maximum cooling ranges from −2.55 to −0.68 K across the models. The abrupt-related results display comparable patterns (Fig. 1e), with maximum cooling ranging from −9.93 to −0.20 K across the models. Furthermore, the results for the same set of “amip” and “abrupt” models indicate that the spatial pattern is consistent with that in Fig. 1, showing robust warming over the mid-latitude Eurasia and North America, along with a distinct surface summer cooling response over the Indian region (Supplementary Fig. 1).

The summer cooling could have contributed to a cooling of more than 0.1 K since 1950. Here, we assume that: (1) the relation between radiative forcing and CO2 concentration is logarithmic; and (2) the radiative forcing-temperature relationship is linear. Using the annual mean of CO2 concentration in 2024 (422.80 ppm)29, the summer cooling in amip-related results corresponds to 0.17 K relative to the pre-industrial level (284.70 ppm) and 0.11 K relative to 1950 (312.0 ppm)30. The corresponding values in abrupt-related results are 0.29 K and 0.23 K, respectively.

This cooling signal is vertically confined to the lower troposphere. Area-averaged profiles over the Indian domain [15–28°N, 72–87°E] indicate that cooling occurs mainly below 850 hPa, transitioning to warming aloft (Fig. 1c, f, Supplementary Fig. 2). Seasonally, the cooling is limited to June–August, coinciding with the Indian summer monsoon (Fig. 2, Supplementary Fig. 3). In September, the model results diverge, and during the rest of the year, India experiences surface warming in response to increased CO2 concentration.

Changes in surface energy fluxes and their drivers
A surface energy budget analysis provides insights into the cooling over India. The reduction in downward solar radiation is the dominant contributor to the surface cooling. Figure 3 displays the response of surface energy fluxes over India, where both the amip-related and abrupt-related results consistently indicate that reduced solar radiation drives the cooling. This decline in downward solar radiation is further linked to enhanced cloud cover (color shadings in Fig. 4a, d). The cloud increase is primarily caused by strengthened atmospheric ascent (contours in Fig. 4a, d). When CO2 concentration rises, and the sea surface temperature is fixed, the Eurasian continent warms (Fig. 1), intensifying the lower-tropospheric southwesterlies toward India (vectors in Fig. 4b, e). The topographic barrier of the Himalayas and the Hindu Kush, together with abundant monsoonal moisture (Fig. 4c, f), favors strong ascent-precipitation feedback, further amplifying vertical motion over India.

Enhanced moisture transport also reinforces cloud formation. The greater continental warming relative to the Indian Ocean strengthens the land-sea thermal contrast, increasing the moisture flux into India (Fig. 4b, e) and promoting local cloud development. Furthermore, the amip-related results indicate that increased CO2 leads to pronounced warming in the upper troposphere, resulting in a more stable troposphere31. This enhanced stability suppresses convective over the southeastern Arabian Sea, the Bay of Bengal, and the South China Sea (Fig. 4a). The weakened tropical convection promotes anomalous moisture convergence toward the Indian subcontinent, thereby favoring cloud development. Notably, such a convective suppression–moisture convergence mechanism is absent in the abrupt-related results (Fig. 4d), probably due to the existence of air-sea interaction.

Among the surface thermodynamics terms, latent heating exhibits a weak cooling tendency but is statistically insignificant in AMIP-related results. The surface cooling reduces the temperature gap between the ground and overlying air, thereby diminishing surface latent heating release. This implies that the changes in surface latent heating are a consequence, rather than a cause, of the surface cooling. Taken together, the CO2-induced enhancement of ascent and moisture transport lead to increased cloud cover and reduced solar heating over India. Similar behavior is found in models of the Coupled Model Intercomparison Project phase 5 as well (ref. 32).

Contributions of near-surface air processes
The thermodynamic budget of near-surface air at 2 m is mainly governed by horizontal advection and adiabatic heating (See “Methods”: Thermodynamic framework for near-surface air temperature at 2 m). The adiabatic term includes sensible and latent heating captured by near-surface air, making its direct evaluation difficult. Figure 5 displays the horizontal temperature advection components. When the sea surface temperature is fixed, the enhanced southwesterlies following the increase in CO2 concentration transport relatively cool air from the Arabian Sea into inland India, producing cold advection (Fig. 4b, e). Area-averaged results over [15–28°N, 72–87°E] show that the contribution of climatological temperature advected by anomalous horizontal wind (V h 0·∇T) is insignificant in amip-related results but significant in abrupt-related results (Fig. 5). In contrast, the anomalous temperature transported by climatological horizontal wind (V h ·∇T0) produces a warming tendency, partially offsetting the cold advection (Fig. 5). The residual terms, inferred from the addition of (V h 0·∇T) and (V h ·∇T0), are small in amip-related results but become comparable to, or even exceed (V h 0·∇T) in abrupt-related simulations.

This near-surface cooling is also modulated by surface processes. In response to the increase in CO2 concentration alone, changes in surface sensible heating tend to favor local warming (Fig. 3). This suggests that near-surface cooling does not directly benefit surface cooling. Instead, in addition to the cold advection led by surface wind change, the surface cooling further contributes to lowering the near-surface air temperature.

Seasonal dependence of the cooling
The occurrence of CO2-induced surface cooling over India exhibits a clear seasonal dependence, confined primarily to the boreal summer. This behavior is closely linked to variations in ambient moisture. Figure 6 demonstrates the annual cycle of column precipitable water averaged over [15–28°N, 72–87°E]. From June to September, the precipitable water levels are higher than those in other months, providing a favorable environment for enhanced convection and the occurrence of ascent-precipitation-cloud feedback. In contrast, during drier months with lower precipitable water, this feedback mechanism is largely suppressed.

Interestingly, although the mean precipitable water in September is comparable to that in June, July and August, surface cooling is insignificant. This discrepancy is mainly attributed to the weaker Eurasian warming in September. In response to the rise in CO2 concentration alone, the surface warming over Eurasia diminishes markedly in September compared with earlier summer months (Supplementary Fig. 3), leading to reduced thermal contrast and weaker upward motion over India. Consequently, the ascent-precipitation feedback and associated cloud enhancement become less effective.

Therefore, the combined influence of abundant atmospheric moisture and strong Eurasian warming during early summer primarily explains the seasonally confined nature of the CO2-induced surface cooling over India.

Absence of cooling over East Asia
Unlike India, East Asia—also a monsoon-dominated region with abundant summer moisture33—does not exhibit CO2-induced surface cooling. This contrast highlights the critical role of regional topography and circulation responses in shaping the thermal effect of greenhouse gas forcing.

Despite comparable levels of summer moisture between East Asia and India (Fig. 4c, f), the response of cloud cover diverges sharply. Under increased CO2 concentration alone, cloud cover decreases over East Asia, preventing the reduction of downward solar radiation that drives surface cooling over India. This suggests that moisture availability alone cannot explain the regional contrast in temperature response.

Topography emerges as a key factor. The Himalayas and the Hindu Kush Mountains, situated north of the Indian subcontinent, act as a barrier that enhances low-level moisture convergence when southwesterly winds intensify under CO2 forcing. This convergence promotes ascent and increased cloudiness over India. In contrast, East Asia lacks such an orographic barrier along the path of anomalous surface winds (Fig. 4b, e), resulting in weaker moisture convergence and limited cloud formation.

Surface wind patterns further contribute to the contrasting temperature responses. Over India, the anomalous southwesterly winds associated with elevated CO2 transport relatively cooler air from the Arabian Sea inland, producing cold advection and reinforcing surface cooling (Fig. 4b, e). Such cold advection is absent over East Asia, where the prevailing wind anomalies do not cross strong temperature gradients.

Together, these results indicate that the distinct topographic configuration of the Himalayas–Hindu Kush region, coupled with differential surface wind responses, underpins the absence of CO2-induced cooling over East Asia. This comparison underscores how regional circulation and terrain jointly modulate the local manifestation of global radiative forcing.

Methods
CMIP6 dataset
This study employs multiple CMIP6 experiments to evaluate the atmospheric response to direct CO2 radiative forcing. Specifically, seven types of simulations are analyzed (Detailed model information is provided in Supplementary Table 1):
(1) preindustrial control (piControl): CO2 concentration is fixed at 284.7 ppm, with a minimum integration length of 500 years36.
(2) abrupt 4×CO2: CO2 concentration is instantaneously quadrupled relative to the preindustrial level and then held constant for at least 150 simulation years36.
(3) abrupt 2×CO2: CO2 concentration is instantaneously doubled relative to the preindustrial level and then held constant for at least 150 simulation years37.
(4) abrupt 0.5×CO2: CO2 concentration is instantaneously halved relative to the preindustrial level and then held constant for at least 150 simulation years37.
(5) amip: Atmosphere-only simulations forced by observed sea surface temperatures and sea ice concentration for 1979–201437.
(6) amip-4×CO2 simulation: same SST and sea ice as amip, but with quadrupled CO2 concentration37.
(7) historical simulation: Simulations forced by observed natural and anthropogenic drivers from 1850–201436.

All model outputs are regridded to a uniform 1°×1° resolution using bilinear interpolation to construct the MME38. A response is considered robust when at least 75% of models agree on the sign of the MME. Some variables are not available, and details of the missing variables are provided in Supplementary Table 2.

To assess model fidelity, simulated surface air temperatures over India are evaluated against the Climatic Research Unit (CRU) v4.09 dataset39. The dataset is bilinearly interpolated to a 1°×1° horizontal resolution. The comparison uses June–August climatology for 1979–2014. Both amip and historical simulations reproduce the observed south–north and west–east temperature gradients across the Indian subcontinent (Supplementary Fig. 4a–c).

Quantitatively, Taylor diagram analysis (Supplementary Fig. 5) shows high spatial correlations with CRU observations. Over [15–28°N, 72–87°E], the spatial correlation coefficients of the amip simulation with CRU are 0.68–0.93 (the average is 0.90); the coefficients of the historical simulation are 0.56–0.98 (the average is 0.86). Most models display a reasonably centered root-mean-square error with CRU. These metrics indicate that the selected CMIP6 models reliably capture the observed Indian surface air temperature distribution, providing a robust basis for subsequent analysis.

CRU observational data
The CRUTS (Climatic Research Unit Time Series) dataset provides a high-resolution, long-term global land climate record, with monthly mean surface air temperature available on a 0.5°×0.5° grid. The dataset integrates observations from the World Meteorological Organization (WMO) CLIMAT reports and NOAA’s Monthly Climatic Data for the World summaries, following strict quality control procedures. Version 4 of the dataset spans 1901–2018 and employs an angular-distance weighting interpolation method, enhancing spatial accuracy and traceability between gridded values and station observations. This ensures the dataset’s suitability for benchmarking model performance in regional climate analyses.

The global precipitation climatology project
To obtain the domain of land monsoon, this study utilizes the globally complete, monthly Global Precipitation Climatology Project dataset40, which provides surface precipitation estimates at a spatial resolution of 2.5° latitude×2.5° longitude from January 1979 onward. The dataset is generated through a merged multi-source approach, integrating low-orbit satellite microwave retrievals, geosynchronous-orbit satellite infrared observations, and surface rain gauge measurements. To optimize accuracy and temporal coverage, the integration strategy employs higher-accuracy microwave data to calibrate the more frequent infrared observations. For the pre-microwave period (prior to mid-1987), the record is extended using infrared-only data adjusted via a calibration derived from later microwave-based analyses. The final satellite-gauge blended product is further corrected using ground-based rain gauge analyses. The archived dataset also includes individual input fields, a merged satellite-only estimate, and associated uncertainty estimates for each variable. This monthly analysis serves as the foundation for the broader suite of GPCP products, including those with higher temporal resolution. In this work, we characterize the 30-year GPCP climatology (1979–2008) and examine its temporal and spatial variability in global precipitation.

Quantification of CO2 radiative forcing effects
To isolate the direct atmospheric response to CO2 radiative forcing, two complementary simulation frameworks are used:
(1) The amip-related simulations. In this kind of simulations41–43, the atmospheric response is estimated from the 30-year climatological difference (1979–2008) between amip-4×CO2 and amip simulations. This configuration excludes oceanic feedbacks, allowing a clean diagnosis of atmosphere–land adjustments to increased CO2.
(2) The abrupt-related simulations. In fully coupled models, global-mean SST variations are regressed out to separate CO2-driven radiative effects from ocean-mediated changes. Specifically, changes in climate variables are regressed against the global-mean SST changes, and the CO2-induced response is quantified as the regression intercept at zero global-mean SST anomaly44,45. Here, the changes are defined as the difference between the first 150 years in abrupt-4×CO2 simulations and the last 100-year climatology in piControl simulations.

Results derived from these two approaches are referred to as “amip-related results” and “abrupt-related results”, respectively. Together, these two approaches serve complementary purposes. The amip-related simulations provide a physically clean estimate of the atmospheric–land adjustments by explicitly excluding SST-mediated feedbacks, whereas the abrupt-related simulations allow the CO2-induced response to be evaluated across a much larger set of models, thereby improving the robustness and physical consistency of the results.

Thermodynamic framework for near-surface air temperature at 2 m
The anomalous thermodynamic energy equation is expressed as46:
∂T0/∂t = −(V h ·∇T0) − (V h 0·∇T) − ω0(γ d − γ) + Q/Cp (1)
where T is temperature, V h denotes the near-surface wind vector, ω represents pressure velocity, γ d is the dry adiabatic lapse rate, γ is the environmental lapse rate, Cp is the specific heat at constant pressure, and Q denotes the diabatic heating. For variable X, X and X0 mean the climatology and anomaly, respectively.

On climate-change timescales, the tendencies and nonlinear advection terms are negligible:
∂T0/∂t ≈ 0 (2)
V 0·∇T0 ≈ 0 (3)

Near the surface, vertical motions are weak (ω ≈ 0), so the vertical adiabatic terms are also negligible. The equation thus simplifies to:
−(V h 0·∇T) − (V h ·∇T0) + dQ/Cp ≈ 0 (4)

Here, the first term represents horizontal temperature advection, and the last term denotes diabatic heating anomalies, which are dominated by radiative and latent heating effects. Although the diabatic term is difficult to evaluate directly, this formulation provides a conceptual basis for decomposing surface temperature responses into advective and radiative components under increased CO2 forcing.

Definition of the global land monsoon region
The global monsoon region is defined as the area where the local “summer minus winter” precipitation rate exceeds 2.0 mm day−1 and where local summer precipitation exceeds 55% of the annual total47. Here, local summer is defined as May–September in the Northern Hemisphere and November–March in the Southern Hemisphere. In this study, the global monsoon region is identified based on the climatological precipitation (1979–2008) from the Global Precipitation Climatology Project40, and only land monsoon regions are considered (Supplementary Fig. 11). The precipitation dataset is bilinearly interpolated to a 1°×1° horizontal resolution.

Data availability
The CMIP6 outputs are available online at https://esgf-node.llnl.gov/projects/cmip6/. The CRUTS v4.09 surface air temperature dataset is available online at https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.09/cruts.2503051245.v4.09/tmp/. The Global Precipitation Climatology Project's monthly precipitation is available online at https://psl.noaa.gov/data/gridded/data.gpcp.html.

Code availability
The source codes used to produce these main results are available from https://zenodo.org/records/18491957. The data in this study were analyzed with the Python programming language.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This work introduces a novel mechanism to explain the observed summer warming hole over India. The study suggests that direct radiative forcing from CO2 causes the Eurasian continent to warm more relative to the surrounding oceans. This differential warming enhances low-level moisture transport and vertical motion over India. The Himalayas and Hindu Kush Mountains act as barriers, trapping moisture and increasing rainfall and cloud cover over India, which results in surface cooling. The robustness of this mechanism offers unique insights into climate responses in India under both global warming and future climate mitigation scenarios. The paper is clearly structured and well-written, making it suitable for publication in Nature Communications after some minor revisions.\n\nIn addition to the CO2-induced warming of the Eurasian continent, there is another mechanism highlighted in Figure 4: the CO2-enhanced static stability of the troposphere. CO2 warms the upper troposphere and increases the troposphere's stability (Held and Soden 2006JC). This stabilization results in decreased convection over the southeast Arabian Sea, Bay of Bengal, and South China Sea (Figure 4a). The suppressed tropical convection leads to moisture convergence and increased rainfall over the Indian subcontinent.\n\n1. The AMIP-style simulations effectively isolate the role of prescribed SSTs. Could the authors clarify the motivation behind also employing the abrupt-4×CO2 and piControl simulations? Providing a clearer justification would help readers understand the experimental design and the added value of this two-pronged approach.\n2. Lines 262–266, the authors suggest that a reduction in CO2 could induce warming over India via the reversal of the direct CO2 effect. A more in-depth discussion here would be beneficial.\n3. Lines 267–281, are the key findings—particularly the summer cooling over India—consistent across different forcing magnitudes, such as in abrupt-2×CO2 or abrupt-0.5×CO2 simulations? Demonstrating the response dependency on CO2 concentration would significantly enhance the generality of the conclusions.", "reviewer_2": "Comments\nReview of “CO2 radiative forcing induces summer cooling over India” by Liu et al.\nThis manuscript evaluates multi-model ensemble (MME) performance using CMIP6 simulation datasets. The results highlight the presence of a warming hole over the North Atlantic and southeastern US showed that increasing atmospheric CO2 concentrations can also induces summertime cooling over India. Due to the direct radiative effect of CO2, warming of the Eurasian continent relative to surrounding oceans enhances low-level moisture transport and vertical motion over India. Combined with abundant summer-monsoon moisture and the topographic blocking effects of the Himalayas and Hindu Kush Mountains, these circulation changes increase cloud cover. The resulting cloud enhancement reduces incoming solar radiation at the surface, producing the observed regional cooling. These results reveal a previously underappreciated mechanism whereby greenhouse gas forcing can paradoxically induce regional cooling through atmospheric dynamical pathways.\n\nHowever, there are some places in which the manuscript may be enhanced. I have a few comments that I will give below:\nSpecific comments:\n1. As mentioned, these studies are relevant but currently missing from the introduction. Temperature changes in the North Atlantic, including the warming hole, influence Indian climate through teleconnection pathways. North Atlantic SST anomalies excite Rossby wave trains and modify jet streams, which alter the large-scale monsoon circulation and affect rainfall and temperature extremes over India. Indian Ocean warming can also impact North Atlantic winds and SSTs, forming an inter basin warming chain that links these regions dynamically. Including these mechanisms clarifies how North Atlantic variability contributes to South Asian climate variability. Please make sure you include the appropriate references.\n2. Lines 98–99 mention the use of AMIP 12 and ABRUPT 48 models. I would like to see Figure 1 for the same set of AMIP and ABRUPT models.\n3. Line 104-105: Please cross check all results values. Over the Eurasia reached ~3 K. Please correct.\n4. Line 108: Maximum temperature over the center region of USA.\n5. Line 113-114: “The ABRUPT results display comparable patterns (Fig. 1e), with maximum cooling ranging from −9.9 to −0.20 K”. Please cross check the values. Figure 1c and 1f are presented with different scales, making it difficult to interpret the relative magnitude of variability. Please revise the plots to use a consistent scale across both panels.\n6. In Figure 1, there are no negative values above –3, and the figure quality is very poor, making it difficult to interpret. Please modify the scale and regenerate the figure to improve visual clarity. Also, cross-verify the stippling. Upon checking Supplementary Figure S2, the monthly AMIP and ABRUPT results show differing model agreement during the JJA months, so it is important to ensure that the values are consistent. Why was a 75% threshold used for model agreement? I would also like to see the results using 90% or 95% thresholds.\n7. In Figure 1 modify the caption. The terms ‘Air temperature (referenced to a pressure level)’ and ‘surface air temperature (referenced to the 2 m level)’ are different. Please check and ensure consistency throughout the text.\n8. In the Methods section, please add references for the simulation details and for the bilinear interpolation method. Please make sure you include the appropriate references.\n9. In Figure 2, What you mean by Error bar denote the 95% confidence intervals across models? Please explain?\n10. Line 157: Please clarify if the \"near surface air temperature\" is at the surface or at 2 meters.\n11. Line 248-250: Add the references?\n12. Line 306: The domain (10–34°N, 70–92°E) was used for the correlation analysis based on CRU observational data, whereas a different domain (15–28°N, 70–87°E) was applied in the other analysis. The rationale for using two separate spatial domains is unclear, leading to inconsistencies in the representation of the Indian region. As a result, the impact of domain selection on the outcomes becomes difficult to interpret and introduces substantial confusion.\n13. Please cross check the correlation and RMSE in the Taylor diagram. Because it is showing very high. As mentioned in lines 305–309, you used CRU observations for the MME. However, the CRU resolution is 0.5° × 0.5°, whereas the model simulations are at 1° × 1° resolution. Please verify this and provide an explanation regarding how the resolution difference was handled.\n14. Line 326: The description of the 30-year climatological difference is unclear. Please specify the exact years defining the climatological baseline to ensure interpretability and reproducibility.\n15. Line 337-350: The thermodynamic energy equations sections. Please provide appropriate references to support it.\n16. In Figure 3, please check the caption. The meaning of “S” is missing shown in the Revise the caption accordingly. Additionally, please cross-check the values of the surface energy fluxes.\n17. In Figure 4, high specific humidity is shown over the Indian region during AMIP compared to ABRUPT. However, Figure 3 shows that latent heat decreases in AMIP compared to ABRUPT. Could this relationship be the opposite? Please cross check?\n18. The shortwave radiation appears higher in ABRUPT, which contradicts the results presented in Figure 5. Please clarify this discrepancy.\n19. Figure 6 shows the area-averaged monthly precipitable water over the Indian domain. Please specify the latitude–longitude coordinates used to define this domain. Which climatological period are you using?\n20. Similarly to Figure 7?\n21. What you mean by the “1st-year following the CO2 quadrupling”? Please explain?\n22. Black dots are depicted in the supplemental Figure S1 across the Himalaya-Tibetan Plateau mountainous regions. There appears to be a problem with the code that needs to be investigated. Furthermore, the resolutions of the AMIP and ABRUPT models differ, resulting in differing orography representations. Please examine this and provide an explanation so that readers understand.\n23. In Figure S2, the latitude–longitude labeling for the selected domain is missing. Add this into the figures.\n24. In Figure S3, I suggest adding bias and correlation plots relative to the observations in this panel.\n25. In the Taylor diagram Figure S4, the AMIP simulations include only 12 models, whereas the historical simulations include 48 models. I am wondering whether it is possible to show the results for the same set of AMIP models applied to the historical simulations. Could you please provide the comparison using the same model sets for both AMIP and historical simulations?\n26. In Figure S5, the surface air temperature responses for the land monsoon domain analysis over the Indian region are missing. Please check and include them in the figure. Unit are missing.\n27. In Figure S6, please explain how this monsoon domain was selected and provide an appropriate reference for it.\n28. The overall quality of the figures needs improvement. Please review all captions, as several panels are missing units or are unclear, and the captions themselves are not well written, making them difficult to interpret. Revise the captions accordingly to ensure clarity and completeness."}, "author_response": "Response to Comments of Reviewer #1\nWe sincerely thank the reviewer for valuable and constructive comments, which have\ngreatly helped us improve the manuscript. In response to the concerns raised, we have\nperformed additional analyses and revised the text accordingly. All modifications are\nhighlighted in red in both the main manuscript and the supplementary information.\nBelow, we provide our point-by-point responses, with the reviewer's original comments\nquoted in italics for clarity.\nReviewer #1 (Comments to the Author):\nThis work introduces a novel mechanism to explain the observed summer warming hole\nover India. The study suggests that direct radiative forcing from CO2 causes the\nEurasian continent to warm more relative to the surrounding oceans. This differential\nwarming enhances low-level moisture transport and vertical motion over India. The\nHimalayas and Hindu Kush Mountains act as barriers, trapping moisture and\nincreasing rainfall and cloud cover over India, which results in surface cooling. The\nrobustness of this mechanism offers unique insights into climate responses in India\nunder both global warming and future climate mitigation scenarios. The paper is\nclearly structured and well-written, making it suitable for publication in Nature\nCommunications after some minor revisions.\nIn addition to the CO2-induced warming of the Eurasian continent, there is another\nmechanism highlighted in Figure 4: the CO2-enhanced static stability of the\ntroposphere. CO2 warms the upper troposphere and increases the troposphere's\nstability (Held and Soden 2006JC). This stabilization results in decreased convection\nover the southeast Arabian Sea, Bay of Bengal, and South China Sea (Figure 4a). The\nsuppressed tropical convection leads to moisture convergence and increased rainfall\nover the Indian subcontinent.\nReply: We have revised the manuscript to include a discussion of the CO -\n2\nenhanced tropospheric static stability and its role in suppressing tropical\nconvection and enhancing moisture convergence over the Indian subcontinent.\n(see line 155-162, page 8)\n1. The AMIP-style simulations effectively isolate the role of prescribed SSTs. Could the\nauthors clarify the motivation behind also employing the abrupt-4×CO2 and piControl\nsimulations? Providing a clearer justification would help readers understand the\nexperimental design and the added value of this two-pronged approach.\nReply: Because the number of model participating in AMIP-related simulations\nare only 12, which may limits the robustness of the results. In light, we employed\nabrupt-4×CO and piControl simulations, in which the number of model\n2\nparticipating is 48.\nWe therefore further estimate the direct atmospheric response using a larger\nset of fully coupled models (abrupt-4×CO and piControl simulations) based on a\n2\ndifferent method. Following previous studies (Gregory et al., 2004; Sherwood et\nal., 2015), to remove the influence of global-mean SST, changes in climate variables\nare regressed against the global-mean SST changes, and the direct atmospheric\nresponse is quantified as the regression intercept at zero global-mean SST changes.\nHere, the changes are defined as the difference between the first 150-year in\nabrupt-4×CO simulations and the last 100-year climatology in piControl\n2\nsimulations.\nTogether, these two methods serve complementary purposes. The AMIP-style\nexperiments provide a physically clean estimate of the atmospheric–land\nadjustments by explicitly excluding SST-mediated feedbacks, whereas the\nregression-based analysis using fully coupled models allows the CO -induced\n2\nresponse to be evaluated across a much larger set of models, thereby improving\nthe robustness of the results. The consistency between the responses diagnosed\nfrom these two independent frameworks increases confidence in our conclusions.\nDetails of the experimental design and methodology have been revised and are\nprovided in the section Quantification of CO radiative forcing effects, which may\n2\nhelp readers better understand the two-pronged approach (see line 94-95, page 5\nand line 381-386, page 19).\nReferences:\nUnderstanding Climate Change. Bulletin of the American Meteorological\nSociety 96, 217-228 (2015).\nclimate sensitivity. Geophysical Research Letters 31, L03205 (2004).\n2. Lines 262–266, the authors suggest that a reduction in CO2 could induce warming\nover India via the reversal of the direct CO2 effect. A more in-depth discussion here\nwould be beneficial.\nReply: The paragraph is rewritten, see line 279-283, page 14. The updated text is\nas follows:\n\"(2) Future CO mitigation scenarios. The projected decline in CO\n2 2\nconcentration aimed at curbing global warming may, counterintuitively,\ncontribute to warming over India. As our results demonstrate that increased CO\n2\nforcing induces summer cooling, reduced CO would thus diminish this cooling\n2\neffect. Consequently, the net regional temperature decrease may be smaller than\nconventionally anticipated.\"\n3. Lines 267–281, are the key findings—particularly the summer cooling over India—\nconsistent across different forcing magnitudes, such as in abrupt-2×CO2 or abrupt-\n0.5×CO2 simulations? Demonstrating the response dependency on CO2 concentration\nwould significantly enhance the generality of the conclusions.\nReply: We repeated the analysis based on abrupt-2×CO and abrupt-0.5×CO\n2 2\nsimulations, both of which display consistent results with present analysis. Please\nsee line 292-296, page 14-15.\nIn the abrupt-2×CO simulation, both the first-year and multi-year mean\n2\nresults indicate that the summer temperature increase over India remains\nrelatively weaker than in other seasons, and a greater fraction of models exhibit\nsummer cooling (Figs. R1a and R1c). Such feature is not evident in most other\nmonsoon regions (Figs. R2). India also shows the larger proportion of models with\na cooling response (Figs. R1b and R1d).\nIn the abrupt-0.5×CO simulation, the first-year results show that, although\n2\nthe MME does not exhibit a distinctly stronger weakening of summer temperature\nover India compared to other seasons, a larger number of models display summer\nwarming (Figs. R3). In the multi-year mean, the weakening of the summer\ntemperature response over India is less pronounced than that in other seasons,\nwith a relatively larger fraction of models exhibiting summer warming (Figs. R4).\nThis slight inconsistency may be related to the limited number of available\nmodels (nine in total) and internal climate variability. Overall, an abrupt change\nin CO concentration can temporarily alter the seasonal temperature response\n2\nover India: summer temperature weakening tends to occur under increased CO\n2\nconcentration, whereas the opposite tendency is observed when CO concentration\n2\nare reduced.\nRelated discussion has been added in the revised manuscript. The following\nfigures are supplemented in Supplementary Information as Supplementary\nFigure 7-10.\nFig R1 Summer cooling response over the Indian surface to increased CO\n2\nconcentration alone. a Monthly surface air temperature response over the Indian\ndomain [15–28°N, 72–87°E] during the 1st-year after CO is doubled. Red line denotes\n2\nthe MME results, and grey lines represent individual model responses. The unit is K. b\nFraction of models exhibiting summer cooling in the 1st-year response. The unit is %.\nc, d Same as (a, b), but for the 1-5 years mean after CO is doubled. The climate\n2\nresponse is evaluated as the difference between the 1st-year or 1-5 year mean of the\nabrupt-2×CO simulation and the last 100-year climatology in piControl simulations.\nexcluding India. a–f Monthly surface air temperature response over the land monsoon\ndomains excluding India for the 1st-year results after CO is doubled. g–l Same as (a–\n2\nf), but for the 1-5 years mean. Red line denotes the MME results, and grey lines\nrepresent individual model results. The results are based on abrupt-2×CO simulation\n2\nand piControl simulation. The unit is K.\nFig R3 Same as Fig R1, but for: (1) the results are based on the abrupt-0.5×CO\n2\nsimulation; (2) the fractions of models exhibiting summer warming are\ndemonstrated in b and d.\nFig R4 Same as Fig R2, but for: the results are based on the abrupt-0.5×CO\n2\nsimulation.\nResponse to Comments of Reviewer #2\nWe sincerely thank the reviewer for valuable and constructive comments, which have\ngreatly helped us improve the manuscript. In response to the concerns raised, we have\nperformed additional analyses and revised the text accordingly. All modifications are\nhighlighted in red in both the main manuscript and the supplementary information.\nBelow, we provide our point-by-point responses, with the reviewer's original comments\nquoted in italics for clarity.\nReviewer #2 (Comments to the Author):\nReview of “CO2 radiative forcing induces summer cooling over India” by Liu et al.\nThis manuscript evaluates multi-model ensemble (MME) performance using CMIP6\nsimulation datasets. The results highlight the presence of a warming hole over the North\nAtlantic and southeastern US showed that increasing atmospheric CO2 concentrations\ncan also induces summertime cooling over India. Due to the direct radiative effect of\nCO2, warming of the Eurasian continent relative to surrounding oceans enhances low-\nlevel moisture transport and vertical motion over India. Combined with abundant\nsummer-monsoon moisture and the topographic blocking effects of the Himalayas and\nHindu Kush Mountains, these circulation changes increase cloud cover. The resulting\ncloud enhancement reduces incoming solar radiation at the surface, producing the\nobserved regional cooling. These results reveal a previously underappreciated\nmechanism whereby greenhouse gas forcing can paradoxically induce regional cooling\nthrough atmospheric dynamical pathways.\nHowever, there are some places in which the manuscript may be enhanced. I have a few\ncomments that I will give below:\nSpecific comments:\n1. As mentioned, these studies are relevant but currently missing from the introduction.\nTemperature changes in the North Atlantic, including the warming hole, influence\nIndian climate through teleconnection pathways. North Atlantic SST anomalies excite\nRossby wave trains and modify jet streams, which alter the large-scale monsoon\ncirculation and affect rainfall and temperature extremes over India. Indian Ocean\nwarming can also impact North Atlantic winds and SSTs, forming an inter basin\nwarming chain that links these regions dynamically. Including these mechanisms\nclarifies how North Atlantic variability contributes to South Asian climate variability.\nPlease make sure you include the appropriate references.\nReply: We added a sentence describing possible causes of the heat extremes and\nincluded 3 additional references. Please see line 76-78, page 4. The sentence and\nreferences are as follows:\n\"... heatwave25. These extreme heat events are closely linked to North Atlantic\nsea surface temperature anomalies, warming of the tropical Indian Ocean, El Niño\nevents, and local land–atmosphere feedbacks26-28. Despite ...\"\nReferences:\nIndian region. Quarterly Journal of the Royal Meteorological Society (2025).\nIncreasing Trends of Heat Waves over India. Scientific Reports 6 (2016).\nextremes in India: unravelling the exceptional 2010 and 2016 hot events.\nClimate Dynamics 60, 3785-3800 (2023).\n2. Lines 98–99 mention the use of AMIP 12 and ABRUPT 48 models. I would like to see\nFigure 1 for the same set of AMIP and ABRUPT models.\nReply: The number of models whose temperature results are available in both\nAMIP-related and ABRUPT-related simulations is 11. Figure R5 displays the\nresults of temperature response based on these 11 models, shown in Fig. R5 (also\npresented as Supplementary Fig. 1). The spatial pattern is consistent with that in\nFig. 1, showing robust warming over mid-latitude Eurasia and North America,\nalong with a distinct cooling response over the Indian region. Furthermore, the\ncooling signal is confined to the lower troposphere. Related discussion has been\nadded in the revised manuscript (see line 117-121, page 6).\nFig. R5 Same as Fig 1, but based on the models whose temperature results are available\nin both AMIP-related and ABRUPT-related simulations, corresponding to all AMIP\nmodels except TaiESM1. The black dots indicate regions where at least 75% of models\nagree on the sign of the MME.\n3. Line 104-105: Please cross check all results values. Over the Eurasia reached ~3 K.\nPlease correct.\nReply: We apologize for the ambiguity in the original manuscript. Here, the value\nrefers to the maximum warming in the MME rather than a regional-mean value.\nTo avoid ambiguity, we have revised the correspongding text (see line 107, page 6).\n4. Line 108: Maximum temperature over the center region of USA.\nReply: Accordingly, we have revised the sentence as follows: “MME maximum up\nto 5.49 K over the center region of United States” (see line 111, page 6).\n5. Line 113-114: “The ABRUPT results display comparable patterns (Fig. 1e), with\nmaximum cooling ranging from −9.9 to −0.20 K”. Please cross check the values.\nFigure 1c and 1f are presented with different scales, making it difficult to interpret the\nrelative magnitude of variability. Please revise the plots to use a consistent scale across\nboth panels.\nReply: We apologize for the inconvenience in the original manuscript.We have\ncarefully cross checked the values, and replotted the figure. Please see Fig. R6 (Fig.\n1 in revised manuscript). Furthermore, we agree that the original wording was\nunclear, we have clarified that the range refers to the maximum cooling across the\nmodels in the revised manuscript (see line 117, page 6).\nFig. R6 Surface air temperature responses to CO radiative forcing. a The response\n2\nof surface air temperature over the global land regions during local summer for the\nAMIP results. b Same as (a), but focusing on the Indian subcontinent. The responses\nare the MME results. The black dots indicate regions where at least 75% of models\nagree on the sign of the MME. c The vertical profile of the area-averaged air\ntemperature response (red line) over the Indian domain [15–28°N, 72–87°E] for the\nAMIP results. Pink shadings indicate the 95% confidence intervals of the MME. These\nare calculated by using ±1.96×𝜎⁄√𝑁 , where 𝜎 denotes the standard deviation\nacross the models and 𝑁 is the number of models. d–f Same as (a–c), but for the\nABRUPT results. The units are K.\n6. In Figure 1, there are no negative values above –3, and the figure quality is very poor,\nmaking it difficult to interpret. Please modify the scale and regenerate the figure to\nimprove visual clarity. Also, cross-verify the stippling. Upon checking Supplementary\nFigure S2, the monthly AMIP and ABRUPT results show differing model agreement\nduring the JJA months, so it is important to ensure that the values are consistent. Why\nwas a 75% threshold used for model agreement? I would also like to see the results\nusing 90% or 95% thresholds.\nReply: We have revised Figure 1 by adjusting the scale to better represent the\nnegative values, regenerating the figure at higher quality to improve visual clarity,\nand carefully cross-verifying the stippling (Fig. R6).\nThe 75% model agreement threshold was employed as a broadly accepted\ncriterion in multi-model ensemble analysis to evaluate the robustness of the\nensemble-mean response. This threshold is commonly used in the literature —\nincluding, for example, work published in Nature sister journals (e.g., Zhou et al.,\n2019, Fig. 1; Kug et al., 2022, Fig. 2) — as it offers a reasonable balance between\nsignal robustness and spatial representativeness, especially given the relatively\nsmall number of models (12) available for the AMIP simulations.\nIn response to the reviewer’s suggestion, we have also examined the results\nusing more stringent agreement thresholds of 90% and 95% (see Figs. R7 and R8).\nAs expected, applying these higher thresholds reduces the number of grid points\nthat meet the criterion in both the AMIP-related and ABRUPT-related simulations,\nparticularly during June–August. Because model responses are influenced by\ninternal variability and differences in model physics, thresholds above 90% may\nbe quite strict and could potentially exclude regions where the forced response is\nphysically consistent but modulated by noise.\nNevertheless, even under the 90% and 95% thresholds, the core spatial\npatterns — especially the cooling signal over India — remain discernible, albeit\nover a more limited area. This indicates that the key findings identified with the\n75% threshold are not substantially altered, supporting the overall robustness of\nour conclusions.\nAssociated references:\nKug, J. S. et al. Hysteresis of the intertropical convergence zone to CO forcing.\nZhou, W. Y., Xie, S. P. & Yang, D. Enhanced equatorial warming causes deep-\ntropical contraction and subtropical monsoon shift. Nature Climate Change 9,\n834-839 (2019).\nFig. R7 Same as Supplementary Fig. 3 in the revised manuscript, but the black dots\nindicate region where at least 90% of models agree on the sign of the MME.\nFig. R8 Same as Supplementary Fig. 3 in the revised manuscript, but the black dots\nindicate region where at least 95% of models agree on the sign of the MME.\n7. In Figure 1 modify the caption. The terms ‘Air temperature (referenced to a pressure\nlevel)’ and ‘surface air temperature (referenced to the 2 m level)’ are different. Please\ncheck and ensure consistency throughout the text.\nReply: We have revised the caption of Figure 1 and carefully checked the\nterminology throughout the manuscript to ensure consistency (see the caption of\nFigure 1 in revised manuscript).\n8. In the Methods section, please add references for the simulation details and for the\nbilinear interpolation method. Please make sure you include the appropriate references.\nReply: We have added the appropriate references in the Methods section for the\nsimulation details and for the bilinear interpolation method. (see line 310-325,\npage 15-16)\nAssociated references:\nPhase 6 (CMIP6) experimental design and organization. Geoscientific Model\nDevelopment 9, 1937-1958 (2016).\n(CFMIP) contribution to CMIP6. Geoscientific Model Development 10, 359-\n384 (2017).\nrecipes 3rd edition: The art of scientific computing. 110-150 (Cambridge,\nUnited Kingdom and New York, NY, USA, 2007).\n9. In Figure 2, What you mean by Error bar denote the 95% confidence intervals across\nmodels? Please explain?\nReply: In Figure 2, the error bars represent the 95% confidence intervals of the\nMME. These are calculated based on ±𝟏.𝟗𝟔×𝝈⁄√𝑵 , where 𝝈 denotes the\nstandard deviation across the models and 𝑵 is the number of models. This has\nbeen clarified in the revised manuscript (see the caption of Figure 2 in revised\nmanuscript).\n10. Line 157: Please clarify if the \"near surface air temperature\" is at the surface or at\n2 meters.\nReply: The “near-surface air temperature” referred to here is the air temperature\nat 2 m. This has been clarified in line 172-174, page 9.\n11. Line 248-250: Add the references?\nReply: Relevant references have been added (see line 268, page 13)\nReferences:\norographic insulation versus plateau heating. Nature 463, 218-222 (2010).\n(2012).\n12. Line 306: The domain (10–34°N, 70–92°E) was used for the correlation analysis\nbased on CRU observational data, whereas a different domain (15–28°N, 70–87°E) was\napplied in the other analysis. The rationale for using two separate spatial domains is\nunclear, leading to inconsistencies in the representation of the Indian region. As a\nresult, the impact of domain selection on the outcomes becomes difficult to interpret\nand introduces substantial confusion.\nReply: For the Taylor diagram, we have now used a consistent domain [15–28°N,\n72–87°E]. The associated figures were all based on this domain. As shown in Fig\nR9 (also presented as Supplementary Fig. 5), both amip and historical simulations\nwell reproduce the spatial distribution of surface air temperature over the Indian\nregion. The spatial correlation coefficients with the CRU observations are 0.68–\n0.93 for AMIP (average 0.90) and 0.56–0.98 for historical (average 0.86). The\nassociated description were updated, see line 334-340, page 16-17.\nFig. R9 Taylor diagram of summer-mean surface air temperature over India.\nTaylor diagrams of surface air temperature over the Indian domain [15–28° N, 72–87°\nE], with the CRU dataset serving as the reference. a and b The Taylor diagram for the\namip and historical simulations, respectively.\n13. Please cross check the correlation and RMSE in the Taylor diagram. Because it is\nshowing very high. As mentioned in lines 305–309, you used CRU observations for the\nMME. However, the CRU resolution is 0.5° × 0.5°, whereas the model simulations are\nat 1° × 1° resolution. Please verify this and provide an explanation regarding how the\nresolution difference was handled.\nReply: We have carefully rechecked the correlation coefficients and RMSE values\nshown in the Taylor diagram, and confirm the accuracy of the calculations\nHowever, in response to previous comment, we modified the focus domain.\nConsequently, the results slightly changes (Fig. R9).\nOn the mismatch of resolution, the CRU observational data are originally\nprovided at a 0.5° × 0.5° horizontal resolution, whereas the model simulations are\nat 1° × 1°. To ensure consistency, the CRU data is bilinearly interpolated to a 1° ×\n1° horizontal resolution. This has been clarified in the revised manuscript (see line\n324-325, page 16).\n14. Line 326: The description of the 30-year climatological difference is unclear. Please\nspecify the exact years defining the climatological baseline to ensure interpretability and\nreproducibility.\nReply: The 30-year climatological baseline is 1979–2008, and this has now been\nexplicitly stated in the manuscript for clarity (see line 372-373, page 18).\n15. Line 337-350: The thermodynamic energy equations sections. Please provide\nappropriate references to support it.\nReply: Appropriate references have been added to support the thermodynamic\nenergy equations (see line 388, page 19).\nReferences:\n57 (Academic press, MA, USA, 2013).\n16. In Figure 3, please check the caption. The meaning of “S” is missing shown in the\nRevise the caption accordingly. Additionally, please cross-check the values of the\nsurface energy fluxes.\nReply: The caption of Figure 3 has been revised to include the meaning of “S”, and\nthe values of the surface energy fluxes have been cross-checked (see the caption of\nFigure 3).\n17. In Figure 4, high specific humidity is shown over the Indian region during AMIP\ncompared to ABRUPT. However, Figure 3 shows that latent heat decreases in AMIP\ncompared to ABRUPT. Could this relationship be the opposite? Please cross check?\nReply: We have verified the results, which are correct. The explanation is as\nfollows:\nThe relationship between mean specific humidity and changes in surface\nlatent heat flux is not necessarily monotonic. This is because latent heat flux is\nconstrained not only by atmospheric moisture but primarily by surface energy\navailability and potential evaporation. To clarify this result, we estimated the\nsurface potential evaporation (PET) using the following established formulation\n(Hamon , 1961; Zhao et al. 2013):\n𝑷𝑬𝑻 = 𝟎.𝟓𝟓𝑫𝟐𝑷𝒕\n𝑷𝒕 = 𝟒.𝟗𝟓𝒆𝟎.𝟎𝟔𝟐𝑻 𝒂/𝟏𝟎𝟎\nWhere D is day time (hours), 𝑻 denotes the surface air temperature.\n𝒂\nThis formulation indicates that PET depends exponentially on surface air\ntemperature. Although AMIP result exhibits higher specific humidity over the\nIndian region compared to ABRUPT result, the surface air temperature response\nin AMIP is weaker. This leads to a smaller decrease in surface potential\nevaporation, which in turn results in a weaker reduction in surface latent heat flux\nrelative to ABRUPT-related simulation (Fig. R10).\nFig. R10 Response of potential evapotranspiration over India. Area-averaged\nsurface pontential evapotranspiration response over Indian domain [15–28°N, 72–\n87°E]. The error bars represent the 95% confidence intervals of the MME. These are\ncalculated based on ±1.96 ×𝜎⁄√𝑁, where 𝜎 denotes the standard deviation across\nthe models and 𝑁 is the number of models. The unit is mm day-1.\nReferences:\nHamon, W. R.. Estimating potential evapotranspiration. Journal of the Hydraulics\nDivision, 87, 107-120 (1961).\nZhao, L. et al. Evapotranspiration estimation methods in hydrological models.\nJournal of Geographical Sciences 23, 359-369 (2013).\n18. The shortwave radiation appears higher in ABRUPT, which contradicts the results\npresented in Figure 5. Please clarify this discrepancy.\nReply: We apologize if we have not fully understood the source of the perceived\ncontradiction. Our current understanding is as follows. The response of shortwave\nradiation primarily pertains to the surface energy budget, whereas the terms\nshown in Fig. 5 represent the thermodynamic budget of the near-surface air. For\nnear-surface air, the absorption and emission of shortwave radiation are quite\ndifferent. Therefore, changes in shortwave radiation are not expected to be\ndirectly balanced by horizontal temperature advection.\n19. Figure 6 shows the area-averaged monthly precipitable water over the Indian\ndomain. Please specify the latitude–longitude coordinates used to define this domain.\nWhich climatological period are you using?\nReply: The Indian domain used in Figure 6 is defined as [15–28°N, 72–87°E]. The\nclimatology of area-averaged monthly precipitable water is computed over 1979–\n2008 for the amip simulations, and over the last 30 years of piControl simulations.\nThese details have been added to the figure caption for clarity (see the caption of\nFigure 6).\n20. Similarly to Figure 7?\nReply: The Indian domain in Figure 7 is defined as [15–28°N, 72–87°E], and the\nfigure caption has been revised (see the caption of Figure 7).\n21. What you mean by the “1st-year following the CO2 quadrupling”? Please explain?\nReply: We apologize for the ambiguity. Here, “the 1st-year following the CO\n2\nquadrupling” refers to the first simulation year after an abrupt quadrupling of\natmospheric CO concentration. We have revised the text to improve clarity by\n2\nreplacing it with “the first year after CO is quadrupled” in the revised manuscript\n2\n(see the caption of Figure 7).\n22. Black dots are depicted in the supplemental Figure S1 across the Himalaya-Tibetan\nPlateau mountainous regions. There appears to be a problem with the code that needs\nto be investigated. Furthermore, the resolutions of the AMIP and ABRUPT models differ,\nresulting in differing orography representations. Please examine this and provide an\nexplanation so that readers understand.\nReply: The black dots appearing over the Himalaya–Tibetan Plateau in\nSupplementary Figure S1 were caused by the plotting code and have now been\ncorrected (Fig. R11, also presented as Supplementary Fig. 2). In addition, the\ndifferences in orography representation between the AMIP and ABRUPT\nsimulations arise from the different horizontal resolutions and the different\nnumber of models, which together affect the MME topography. These points have\nbeen clarified in the revised figure caption so that readers can understand the\nsource of the orography differences (see the caption of Supplementary Fig. 2).\nFig. R11 Air temperature response. a–e Air temperature responses at different\npressure levels from the AMIP results. f–j Same as (a–e), but for the ABRUPT results.\nBlack dots denote the regions in which at least 75% of models agree on the sign of the\nMME, representing robust signals. Blank areas indicate topography, differences in the\nblank areas between the AMIP and ABRUPT results are due to differences in model\nresolution and the number of models in each ensemble. The unit is K.\n23. In Figure S2, the latitude–longitude labeling for the selected domain is missing.\nAdd this into the figures.\nReply: As shown in Fig. R12 (also presented as Supplementary Fig. 3), the\nlatitude–longitude labeling for the selected domain has now been added.\nFig. R12 Monthly responses of surface air temperature. a–l Responses of surface\nair temperature for each calendar month from the AMIP results. m–x Same as (a–l) but\nfor the ABRUPT results. Black dots indicate regions where at least 75% of models agree\non the sign of the MME, indicating robust signals. The unit is K.\n24. In Figure S3, I suggest adding bias and correlation plots relative to the observations\nin this panel.\nReply: Following this comment, bias plots has been explicitly included and is\nshown in Figs. R13d and R13e (also presented as Supplementary Fig. 4). In\naddition, we are not entirely sure which type of “correlation plots” is being\nreferred to. In our analysis, the spatial correlation between the simulated and\nobserved surface air temperature over the Indian domain [15–28°N, 72–87°E] is\nquantified using the Pattern Correlation Coefficient (PCC), which is already\nshown in Fig. R13b and R13c.\nFig. R13 Climatology of summer-mean surface air temperature over land. a\nSurface air temperature from the CRU datasets. b Same as (a), but for the MME results\nof amip simulations. c Same as (a), but for the MME results of historical simulations.\nd Bias of the summer-mean surface air temperature in the MME results of amip\nsimulations relative to CRU. e Same as (d), but for the MME results of historical\nsimulations. The Pattern Correlation Coefficient (PCC) shown in (b) and (c) is\ncalculated over the Indian domain [15–28°N, 72–87°E]. The analysis focuses on the\nperiod 1979–2014 and the boreal summer months (June–August). The units are °C.\n25. In the Taylor diagram Figure S4, the AMIP simulations include only 12 models,\nwhereas the historical simulations include 48 models. I am wondering whether it is\npossible to show the results for the same set of AMIP models applied to the historical\nsimulations. Could you please provide the comparison using the same model sets for\nboth AMIP and historical simulations?\nReply: Following the comment, we have repeated the analysis using the same set\nof 11 models that are available for both the amip and historical simulations.\nWe added a comparison of the summer-mean surface air temperature climatology\nbetween observations, amip, and historical simulations based on the same model\nset (Fig. R14), and then constructed the Taylor diagram using the same 11 models\n(Fig. R15). Same model sets for both AMIP and historical simulations also reliably\ncapture the observed Indian surface air temperature distribution.\nFig. R14 Same as Fig. R13, but for all participating AMIP models except TaiESM1.\nFig. R15 Same as Fig. R9, but for all participating AMIP models except TaiESM1.\n26. In Figure S5, the surface air temperature responses for the land monsoon domain\nanalysis over the Indian region are missing. Please check and include them in the figure.\nUnit are missing.\nReply: We apologize for the lack of clarity in the original caption that may have\ncaused confusion. The surface air temperature response over the Indian land\nmonsoon domain is indeed included in our analysis and is shown in the revised\nmanuscript (Figs. 7a and 7c). Previous Figure S5 is intended as a supplementary\nfigure to illustrate the responses over the other land monsoon domains for\ncomparison, but this was not clearly stated in the original caption. We have now\nrevised the caption of previous Figure S5 to explicitly clarify the domains included\nand added the missing units to panels (Fig. R16, also presented as Supplementary\nFig. 6).\nFig. R16 Monthly surface air temperature responses in land monsoon domains\nexcluding India. a–f Monthly surface air temperature response over the land monsoon\ndomains excluding India for the 1st-year results after CO is quadrupled. g–l Same as\n2\n(a–f), but for the first 1-5 year mean. Red line denotes the MME results, and grey lines\nrepresent individual model results. The results are based on abrupt-4×CO simulation\n2\nand piControl simulation. The land monsoon domains are defined as shown in\nSupplementary Figure 11. The unit is K.\n27. In Figure S6, please explain how this monsoon domain was selected and provide an\nappropriate reference for it.\nReply: In the original version, the monsoon domains in Figure S6 were roughly\nselected using approximate rectangular boxes. To follow a more widely accepted\nand scientifically justified definition, we now define the global land monsoon\nregion based on the widely used approach. Specifically, the global monsoon region\nis defined as the area where the local “summer minus winter” precipitation rate\nexceeds 2.0 mm day-1 and where local summer precipitation exceeds 55% of the\nannual total (Wang et al., 2012). Here, local summer is defined as May–September\nin the Northern Hemisphere and November–March in the Southern Hemisphere.\nIn this study, the global monsoon region is identified based on the climatological\nprecipitation for the period 1979–2008 from the Global Precipitation Climatology\nProject (GPCP; Adler et al., 2003), and only land monsoon regions are considered.\nThe precipitation data is bilinearly interpolated to a 1° × 1° horizontal resolution.\nAs shown in Fig. R17 (also presented as Supplementary Fig. 11), this figure\nillustrates the global land monsoon domains obtained using this approach,\nexcluding the South Asian Monsoon region. The changes in the definition of the\nmonsoon region do not affect the results (see Supplementary Fig. 6 in the revised\nmanuscript). The corresponding method and references have been added in the\nrevised manuscript (see line 352-367, page 17-18 and line 408-415, page 20).\nFig. R17 Global land monsoon domains. Land monsoon domains excluding India are\nshown as defined in the Method section, based on climatological precipitation (1979–\n2008) from Global Precipitation Climatology Project. The Indian region is defined over\nthe domain [15–28°N, 72–87°E], only land regions are considered.\nReferences:\n(GPCP) monthly precipitation analysis (1979-present). Journal of\nHydrometeorology 4, 1147-1167 (2003).\nin the tropics. Dynamics of Atmospheres and Oceans 44, 165-183 (2008).\n28. The overall quality of the figures needs improvement. Please review all captions, as\nseveral panels are missing units or are unclear, and the captions themselves are not\nwell written, making them difficult to interpret. Revise the captions accordingly to\nensure clarity and completeness.\nReply: We thank the reviewer for pointing out the issues regarding figure quality\nand captions. We have carefully reviewed all figures and revised the captions to\nensure clarity and completeness, including adding units where missing, clearly\nlabeling all panels, and making each caption clear and independent so that the\nfigures can be easily interpreted. We have also improved the readability of the\nfigures by adjusting color scales, font sizes, and resolution where necessary."}, {"round": 2, "reviewer_comments": {"reviewer_2": "The authors have thoroughly addressed all comments, and the revised manuscript meets the required standards. This article is recommended for publication."}, "author_response": ""}]

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