Stronger ENSO-induced global SST variability in a warming climate

Domain: physics
Published: 2026-03-19
Editorial outcome: major_revision

Abstract
The El Niño-Southern Oscillation (ENSO) is a leading mode of interannual climate variability with far-reaching global impacts. Understanding how ENSO-driven changes evolve in a warming climate is essential to project future climate variability. Here, we show that climate models robustly project an amplification of ENSO’s influence on global sea surface temperature (SST) under greenhouse warming. This amplification is primarily driven by two factors: changes in El Niño-induced surface wind speed and alterations in the climatological air-sea humidity difference. The former is linked to enhanced atmospheric teleconnections associated with ENSO, while the latter stems from an overall increase in global SST. Our findings suggest that future El Niño events may exert stronger regional climate impacts, not only through intensified atmospheric teleconnections but also by reinforcing local air-sea interactions.

Introduction
The El Niño-Southern Oscillation (ENSO) is the most dominant mode of interannual climate variability, exerting widespread influence on global climate patterns via atmospheric teleconnections1–3. These ENSO-induced teleconnections are crucial processes that drive regional extreme weather events, such as floods, droughts, and heavy rainfall worldwide4–7. Since El Niño’s characteristics and teleconnections depend on mean climate states, their impacts are expected to change significantly under global warming8,9.

In recent decades, numerous studies have pointed out that El Niño-like warming modifies atmospheric responses to the equatorial Pacific sea surface temperature anomalies (SSTA)10–12. The relatively enhanced sea surface temperature (SST) warming over the eastern Pacific promotes both the eastward migration and intensification of convection13–15. Additionally, precipitation responses will become highly sensitive to SSTA according to their non-linear relationship14,16,17, leading to the strengthening of convection, which in turn amplifies ENSO teleconnections. For instance, teleconnection patterns associated with the Pacific-North American (PNA) and Pacific-South American (PSA) patterns are projected to shift eastward and intensify with increased CO₂ forcing18–20. Moreover, the changes in position and strength of the westerly jet under greenhouse warming are expected to modulate these teleconnections by altering the midlatitude waveguide21,22. Consequently, these intensified ENSO-driven atmospheric teleconnections are likely to exacerbate regional climate impacts, leading to more frequent and severe extreme weather events23,24.

ENSO-driven atmospheric teleconnections influence regional climate directly and also indirectly through changes in regional SST, further amplifying its impacts on local climate patterns via complex air-sea interactions. ENSO-induced atmospheric circulation alters anomalies in near-surface temperature, humidity, and winds, which subsequently affect SST through ocean dynamics and surface energy fluxes25–27. Ocean dynamics include processes such as vertical mixing and horizontal advection, while surface energy fluxes refer to heat exchanges between the ocean and atmosphere, primarily through shortwave radiation and latent heat flux. These mechanisms illustrate how ENSO influences regional SST, subsequently shaping local climate patterns28.

Interannual SST variability driven by ENSO plays a pivotal role in not only shaping local climate patterns but also regulating marine ecosystems and biogeochemical cycles, influencing processes such as coral bleaching, fishery productivity, and oceanic CO₂ uptake29–31. These widespread impacts underscore ENSO's ecological and environmental significance. Under global warming, the interaction between ENSO-induced SSTA and its subsequent effects on regional climate is expected to intensify. Elevated mean SST globally allows even minor SST variations to exert stronger influences on atmospheric circulation, thereby amplifying ENSO's impacts on regional climate patterns14,16,32,33. Given these potential changes, it is essential to investigate how ENSO’s influence on regional SST may evolve in a warmer climate. However, despite the critical role of ENSO-induced SST in shaping climate and ecological systems, limited attention has been given to understanding how ENSO-induced SST changes in distant regions might respond to global warming.

To address this issue, we investigate changes in ENSO impacts on global SST and underlying mechanisms using the global warming scenario. We conduct a comprehensive regional analysis and demonstrate that the ENSO impacts on SST in most regions are intensified due to both enhanced ENSO teleconnections and changes in climatological mean states.

Results
Changes in El Niño-induced global SST
To examine the spatial pattern and magnitude of the El Niño impacts on global SST variability, we calculated the explainable variance (R2)34. We first calculated the lead-lag correlations between the 3-month running mean SSTA and the December-February (D(0)JF(1)) Niño3.4 SSTA (DJF Niño3.4). Then, we identified the maximum R2 at each grid point from June-August (JJA(0)) to the following June-August (JJA(1)) (see Methods for details). Figure 1a shows the result from the CESM1 present-day (PD) simulation, revealing the highest R2 values in the equatorial Pacific, attributable to autocorrelations in this region. Additionally, high R2 values are found in both the tropical Indian and Atlantic Ocean35, similar to the observational pattern, exhibiting pattern correlations ranging from 0.75 to 0.78 based on three observational datasets (Supplementary Fig. 1). In mid-to-high latitudes, the R2 distribution reflects SST responses driven by ENSO teleconnections, such as PNA and PSA patterns (Fig. 1a). The similarity to the observations suggests that the current model reasonably reproduces the impacts of El Niño on global SST.

In the greenhouse warming scenarios, the spatial pattern of R2 remains largely similar to that in the PD simulation. However, the R2 values exhibit significant increases over most of the global ocean (Fig. 1b), indicating an amplification of El Niño impacts on global SST. Figure 1c highlights the difference between the present and future climates. It is clear that the pronounced increase is especially apparent in the subtropical north and south Pacific, the western tropical Atlantic, the North Atlantic Ocean off the U.S. East Coast, the south Indian Ocean (SIO), the East China Sea (EastCS), and the southern region of the Australian continent. While the tropical Indian Ocean experiences a notable weakening of the ENSO impact (Fig. 1c), SST responses are generally strengthened globally, suggesting an enhanced potential for ENSO to influence regional climates.

To ensure the robustness of these findings and avoid a single model dependency, we analyzed 34 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) pre-industrial and 1pctCO scenarios (Fig. 1d-f). The CMIP6 Multi-Model Ensemble mean (MME) exhibits an overall significant increase in the R2, consistent with the CESM1 results, although the SST response in the CMIP6 MME is slightly weaker than in CESM1 (Fig. 1d), likely due to multi-model averaging. Notably, R2 associated with ENSO is amplified at every grid point in the CMIP6 MME results (Fig. 1f). This consistent amplification across all models underscores the robustness of the enhanced ENSO impacts on global SST, which might be driven by fundamental dynamical processes.

To quantify the changes in ENSO impacts, we calculated the global mean of the absolute values of regression coefficients between the maximum R² period SSTA and DJF Niño3.4 for each grid (hereafter referred to as SST ; see Methods for details). As shown in Figure 2a, SST increases significantly under greenhouse warming by approximately 13% compared to the PD simulation. This result is consistent across all 34 CMIP6 models, with each model showing a statistically significant increase (Supplementary Fig. 2 and 3).

The mechanisms of amplified SST variability
We adapted the mixed-layer heat budget equation to decompose the contributions to the enhanced El Niño impacts. Previous studies have identified surface flux (SFC; positive into the ocean) as a primary driver of SST responses to ENSO25,36,37. To quantify the contribution of the SFC, we first define SST as the time-integrated the surface flux-related term of the equation, which is determined by (i) El Niño-induced surface flux anomaly and (ii) climatological mixed layer depth (MLD, see Methods for details) Figure 2a shows that the magnitude of the SST is larger than SST in both PD and 2100, possibly due to the damping role of the ocean dynamics. These results suggest that the SFC is a primary driver inducing ENSO-driven global SSTA at least on a global average. Additionally, the changes in SST between PD and 2100 are comparable to the change in SST , indicating that the amplified El Niño impacts can be attributed to anomalies induced by SFC.

To separate the mechanism, we decompose SST into (i) SST , which holds MLD fixed at its PD climatology, and (ii) SST , which holds the surface flux anomaly (SFC anomaly regressed onto DJF Niño3.4) fixed at its PD value (see Methods for details). Since the MLD tends to be shallower due to enhanced upper ocean stratification under global warming38 (Supplementary Fig. 4), the SST responses to a given SFC forcing can be amplified owing to the reduced mixed-layer heat capacity39. However, the ∆SST explains only ~10% of the ∆SST (yellow bar in Fig. 2b). As a result, ∆SST substantially contributes to the amplification of the impacts on SST, accounting for ~90% of the change (green bar in Fig. 2b).

Next, we further decomposed SST into three components: radiation (SST ), sensible heat flux (SST ), and latent heat flux (SST ). Figure 2c illustrates the contributions of each component to SST . In the present climate, radiation and latent heat flux are the primary contributors to the ENSO-induced SSTA. Notably, ∆SST is predominantly due to changes in ENSO-induced latent heat flux (~50%) and radiation (~37%). The increase in ∆SST is largely attributed to ENSO-induced longwave radiation, which is largely compensated by shortwave radiation (Supplementary Fig. 5). The increase in longwave radiation is likely driven by enhanced ENSO-induced local SST anomalies, with higher atmospheric moisture content and temperature. Stronger SST anomalies amplify downward longwave radiation by increasing atmospheric absorption, thereby reinforcing the radiative feedback process.

Importantly, ΔSST is the most significant contributor, underscoring the critical role of changes in the evaporation process in amplifying ENSO impacts on SST. Under global warming, the atmospheric water vapor response intensifies markedly due to the Clausius–Clapeyron relationship33. As mean temperature rises, the atmosphere's capacity to hold moisture increases by approximately 7% per 1 K of warming, leading to substantially larger mean moisture. This enhanced moisture capacity amplifies latent heat flux responses, making evaporation a key factor in understanding the intensified ENSO-driven climate variability in a warmer world. Building on previous research that highlights the importance of water vapor response33, our study focuses on latent heat flux as a crucial element in explaining the intensified ENSO-induced SST responses.

Why are ENSO-induced latent heat flux anomalies amplified in the future climate? We first investigated changes in wind speed associated with ENSO by calculating the lead-lag regression of the 3-month running mean surface wind speed anomaly with respect to DJF Niño3.4 SSTA from MAM(0) to the following JJA(1), identifying the maximum absolute value at each grid point for both the PD and 2100. As shown in Fig. 3a, surface wind speed increases over the extratropical Pacific, the Indo-Pacific warm pool region, and the subtropical Atlantic Ocean, while it decreases over the equatorial central Pacific, the western Indian Ocean, and the northern off-equatorial Atlantic, which closely resembles the observed pattern40. Under greenhouse warming, the spatial pattern remains generally similar to that of the PD, but the magnitudes are enhanced (Fig. 3b). This enhancement is clearly depicted in the difference map (Fig. 3c and Supplementary Fig. 6), indicating that the wind-evaporation processes41 will be intensified over most of the global oceans via atmospheric teleconnection.

The intensified El Niño–related wind responses are likely linked to enhanced convective responses driven by ENSO SST forcing. As shown in Fig. 3d, positive precipitation anomalies over the central and eastern Pacific become stronger and extend farther eastward under greenhouse warming. Such intensified precipitation responses have been documented in previous studies, which proposed plausible physical mechanisms42. First, atmospheric warming increases column water vapor at approximately the Clausius–Clapeyron rate, so that a given SST anomaly produces stronger precipitation and diabatic heating16,43. Second, El Niño–like mean state changes favor an eastward extension of ENSO-related convection anomalies13,14,18,44. Consequently, the same El Nino-related SST anomaly induces a larger atmospheric response—manifested as stronger convective heating and circulation changes under warming33. This intensified atmospheric circulation, in turn, leads to stronger anomalous surface wind speeds, thereby modulating evaporation through enhanced air–sea interaction.

Moreover, the latent heat flux depends on the mean specific humidity difference between the sea surface (q ) and the adjacent air (q ). Under global warming, this climatological air-sea specific humidity difference (δq = q - q ) becomes larger, resulting in a greater latent heat flux response to a given wind speed anomaly. Figures 3e-g clearly show that climatological δq increases over most global oceans, except for the subpolar North Atlantic. It is well known that the saturation specific humidity at the sea surface increases almost exponentially with rising SST following the Clausius-Clapeyron relationship. Although the q increases, the q increases more rapidly, leading to an overall increase in the climatological δq as SST rises. In this regard, the decreased mean δq in the subpolar North Atlantic (Fig. 3g) is also explained by the North Atlantic warming hole45,46. With the larger background δq and enhanced wind speed anomalies associated with ENSO, significantly larger latent heat flux anomalies will occur over most of the ocean surface.

While Figures 2 and 3 provide a global overview of sensitivity changes, a regional perspective reveals that El Niño–induced SST anomalies intensify under warming across four representative basins—the East China Sea (EastCS), the U.S. East Coast, the South Indian Ocean (SIO), and the subtropical North Pacific (SNP)—with the amplification primarily driven by ENSO-induced latent heat flux changes (ΔQ' ) (Fig. 4; Supplementary Fig. 7). In the EastCS, an intensified western North Pacific anticyclone during D(0)JF(1)–MAM(1) weakens the climatological northwesterlies and reduces evaporation, yielding warmer SSTA (Fig. 4b; Supplementary Fig. 8 and 9). Along the U.S. East Coast, stronger ENSO-induced westerlies from ASO(0) to JFM(1) increase wind speed and enhance local evaporation, producing pronounced cooling that peaks in FMA(1) (Fig. 4c; Supplementary Figs. 8, and 9). In the SIO, by contrast, evaporation decreases, and warmer SSTAs develop (Fig. 4d), primarily governed by increases in climatological δq rather than by ENSO-induced wind speed (Supplementary Fig. 9 and 11). The SNP experiences cooler SST (Fig. 4e), as strengthened ENSO-induced winds align with the trade winds from ND(0)J(1) to AMJ(1), increasing wind speed and evaporation; here, ENSO-induced wind speed changes lead, whereas ENSO-induced δq acts as a damping (Supplementary Fig. 8-10). Across regions, the roles of MLD changes are secondary. Collectively, these results indicate that changes in the background δq—which modulate the efficacy of latent heat fluxes—together with stronger, region-dependent ENSO-induced wind anomalies, lead to an overall amplification of El Niño’s SST impacts (see Supplementary Note for quantitative shares and seasonal phasing).

Methods
Model data and experiment design
This study employed the Community Earth System Model version 1.2.2 (CESM1) for ensemble analysis. The atmosphere and land models have a horizontal resolution of approximately 1° and 30 vertical levels, whereas the sea-ice and ocean models operate with a nominal horizontal resolution of 1° and 60 vertical ocean levels. Climate simulations were conducted for both present and future conditions. The control experiment was run with a fixed atmospheric CO concentration of 367 ppm over 900 years, using the last 400 years for the present-day (PD) simulation. The future simulation (1pctCO2) utilized an escalating atmospheric CO concentration with a rate of 1% increase per year over 140 years, until it quadrupled the initial CO concentration (1468 ppm). We used model years from 2085 to 2115 referred to as 2100 in this study (after 85 years and 115 years from 367 ppm). Across the period, the average CO concentration is approximately 997 ppm, which corresponds to the high-emission Shared Socioeconomic Pathways 5-8.5 scenario. There are 28 ensembles that start from a different initial condition from the PD experiment.

In addition, 34 models in the CMIP6 pre-industrial and 1pctCO2 scenarios are employed, using one ensemble member per model for each experiment. These scenarios are reasonably similar to the previously mentioned PD and 1pctCO2 simulation in CESM1, but not in the initial CO concentration (284.7 ppm). Used models are summarized in Supplementary Table 1.

Observation data
We utilized observational SST data from the Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST), NOAA Optimum Interplated Sea Surface Temperature (OISST), and Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5) for the years 1981-2023. All the observed SST dataset is used after interpolating to 1°×1° horizontal resolution.

Definition of ENSO and teleconnection
The DJF SSTA, averaged over the Niño3.4 region (170°E-120°W, 5°S-5°N), is used as an indicator of ENSO. The SSTA is calculated by subtracting the monthly linear trend and climatological mean from the SST. To isolate ENSO teleconnection impacts on SST, we excluded the equatorial Pacific region (150°E–80°W, 10°S–10°N) from the global mean calculation, allowing for a more precise evaluation of the SST response to ENSO-induced teleconnections.

Teleconnection impacts on SST
A 3-month running mean was applied to the SST, SFC, and MLD data, covering the period from MAM(0) to the following JJA(1). The maximum R² value was determined by analyzing the lead-lag correlation between the DJF Niño3.4 SSTA and the global SSTA. Establishing the MAM(0) period as the baseline, we evaluate the correlation changes within the timeframe between JJA(0) and JJA(1) (excluding MAM(0)-MJJ(0)). Within this interval, we select the period with the most significant deviation in correlation from the MAM(0) baseline, denoted as 𝜏. The explainable variance corresponding to 𝜏 is then extracted and designated as the maximum R² value. By concentrating on the interval with the most pronounced correlation change, the maximum R² value serves as a critical indicator of the strength of the teleconnection between the ENSO and global SST variabilities.

We calculated the surface flux-induced SSTA in diagnostic framework, based on the following mixed layer heat budget equation:
𝜕𝑇 𝑄
m net
= +Res, (1)
𝜕𝑡 𝜌 𝐶 ℎ
0 p
where T is the mixed layer temperature, Q is net surface flux (positive into the ocean), 𝜌
m net 0
(1027 kg m-3) is the reference density of seawater, C (3985 J kg-1 K-1) is the specific heat of
p
seawater at constant pressure. We only used surface flux term and calculate the surface flux contribution to ENSO-driven SSTA. The Res mainly represents contributions from ocean dynamical processes such as horizontal/vertical advection, entrainment, and diffusion.

SST , SST , SST is calculated as follows:
SFC+MLD SFC MLD
𝜏 𝑄′
SST = sign (SST )×∫ net dt, (2)
SFC+MLD TOT 𝜌 𝐶 ℎ̅
MAM(0) 0 p

𝜏 𝑄′
SST = sign(SST )×∫ net dt, (3)
SFC TOT 𝜌 𝐶 ℎ̅
MAM(0) 0 p PD

𝜏 𝑄′
net,PD
SST = sign(SST )×∫ dt, (4)
MLD TOT 𝜌 𝐶 ℎ̅
MAM(0) 0 p
where, SST is the difference between the regression coefficient of SSTA with respect to DJF
TOT
Niño3.4 SSTA at time 𝜏 and MAM(0), Q’ is the regression coefficient of SFC anomaly with
net
respect to DJF Niño3.4 SSTA during MAM(0) to following JJA(1), and ℎ̅ is climatological MLD. We perform the time-integration of the surface flux term of Eq. (1) to measure SST anomaly generated by the ENSO-induced SFC. SST and SST were calculated the same as SST
SFC MLD SFC+MLD
but ℎ̅ and Q’ are fixed at their PD simulation, respectively. To capture the absolute magnitude
net
of each contribution, the integrated terms were multiplied by the sign of SST . SST , SST ,
TOT RAD SH
and SST are computed in the same manner of SST , replacing the radiative (Q' + Q' ),
LH SFC SW LW
sensible (Q' ) and latent (Q' ) flux anomalies, respectively.
SH LH
For the maximum wind speed forcing, we identify the month with the largest absolute regression onto DJF Niño3.4 SSTA within MAM(0)-JJA(1) (including MAM(0)-MJJ(0)). We allow this to differ from τ because surface flux forcing generally precedes the SST response, so its timing may lead τ.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Review of the manuscript entitled \"Stronger ENSO-induced Global SST Variability in a Warming Climate\"\nIn this study, the authors explore how greenhouse warming modulates the influence of ENSO on global SST variability.\nThey show that the ENSO-driven SST variability is expected to amplify in a warmer climate, primarily due to the increase in\nEl Niño-induced surface wind speed anomaly and the climatological air-sea humidity difference. The study highlights that\nthese changes arise from the strengthened ENSO teleconnections and overall warming of the global oceans, respectively.\nThe findings provide important new insights into the mechanisms that could intensify regional climate impacts of future El\nNiño events. Overall, the manuscript is well-organized and presents several novel findings. I believe it could be suitable for\npublication after some major revisions. My specific comments are as follows:\nMain comments:\n1. Although this manuscript primarily focuses on the impact of unit-strength ENSO events on global SST variability, it is the\noverall influence of ENSO on the climate system that is of greater concern, particularly for policy-making and climate risk\nmanagement. While the authors briefly discuss the effects of ENSO amplitude changes at the end of the manuscript, I\nsuggest that they should present the total change in ENSO’s influence on global SST and quantify the relative contributions\nof two factors: (a) changes in the sensitivity of SST to unit-strength ENSO events, and (b) changes in ENSO amplitude. Such\nan analysis would help determine whether the former plays the dominant role in amplifying ENSO’s impact on SSTs. Recent\nstudies (Lin et al. 2024; Wu et al. 2021) have employed a regression-based method to disentangle the contributions of\nENSO amplitude changes and climate sensitivity to ENSO in shaping ENSO’s influence on regional atmospheric circulation\nand SST variability. I recommend that the authors refer to these approaches and consider adopting a similar methodology.\n2. The authors attribute the enhanced SST variability associated with ENSO primarily to the strengthening of surface flux\nanomalies, particularly latent heat flux (LHF). Based on qualitative analysis, they argue that the increase in global-mean\nLHF anomalies is driven by both enhanced climatological air-sea humidity differences and strengthened ENSO-induced\nsurface wind anomalies. However, for several key regions, the analysis lacks sufficient quantitative support to convincingly\nidentify the dominant physical drivers. Merely showing the spatial patterns of total LHF and wind anomalies is not adequate\nto substantiate the claim (Lines 201–203) that “the East CS, U.S. East Coast, and SNP are primarily attributed to anomalous\nwind variations, whereas the SIO is influenced by humidity changes.” For instance, over the U.S. East Coast, the\nclimatological specific humidity contrast (qs – qa) shows a substantial increase, which could potentially contribute more to\nthe LHF anomalies than wind speed changes. Similarly, the changes in the SIO region may not be solely governed by the\nincrease in anomalous humidity gradient. I therefore strongly recommend that the authors perform a more detailed\nquantitative analysis using the bulk formula for LHF to examine the contributions from changes in wind speed anomaly,\nclimatological qs – qa, and ENSO-induced humidity gradient anomaly, and other relevant factors. The authors could refer to\nLin et al. (2024) for relevant diagnostic approaches and analyses.\n3. It is well known that El Niño and La Niña are asymmetric in terms of the center of SSTA, amplitude, persistence, and other\ncharacteristics, leading to their distinct and asymmetric impacts on the global climate. However, the current analysis is\nprimarily based on correlation or regression methods, which implicitly assume linearity and overlook the influence of ENSO\nasymmetry. Therefore, I think that the authors should examine the composite changes in global SST separately for El Niño\nand La Niña events. At the very least, they should acknowledge this limitation and provide a brief discussion of its potential\nimplications for their results.\n4. I noticed that the equatorial Indian Ocean exhibits a significant decrease in ENSO-induced SST variability. However, both\nsurface wind speed and the climatological humidity gradient in this region are projected to strengthen under global warming,\nwhich suggests that changes in LHF anomalies alone may not fully explain the reduced SST variability there. Do the authors\nhave a plausible explanation for this contrasting behavior? What mechanisms might be responsible for the weakened\nENSO-induced SST variability in this region, especially given the overall strengthening of ENSO impacts observed\nelsewhere?\nReferences:\nLin, S., B. Dong and S. Yang, 2024: Enhanced impacts of ENSO on the Southeast Asian summer monsoon under global\nwarming and associated mechanisms. Geophysical Research Letters, 51, e2023GL106437. DOI: 10.1029/2023GL106437\nWu, M., Zhou, T., & Chen, X. (2021). The source of uncertainty in projecting the anomalous western North Pacific\nanticyclone during El Niño–decaying summers. Journal of Climate, 34(16), 6603–6617. https://doi.org/10.1175/JCLI-D-20-\n0904.1\nSpecific Comments:\nLines 40–42: Consider citing some references here to support the statement, such as recent studies on the enhanced impact\nof ENSO on East Asian climate under global warming (e.g., Lin et al. 2024).\nLines 57–59: I also suggest citing more recent literature to support this point.\nLine 72: It is unclear whether this refers to a 3-month mean SSTA or a 3-month running mean SSTA. This should be\nclarified, as the method section (Line 281) seems to indicate the latter.\nLines 133–138 & Extended Data Fig. 4: It is difficult to conclude from this figure that the increased SFC are dominated by\nlongwave radiation induced by ENSO. Please add the difference between the 2100 and PD to help support this claim.\nLine 144: Consider changing “moisture anomalies” to “mean moisture,” if referring to the background state.\nLines 165–166: Do you mean the mean or the anomaly of the specific humidity difference (qs – qa)? I believe the intended\nmeaning may be the change in the climatological humidity gradient. Please clarify.\nExtended Data Fig. 5: The labels are too small, making the figure difficult to read. The authors may want to adjust the panel\nlayout to enlarge each subplot and improve legibility.\nLines 186–203: The change in latent heat flux anomalies can result from variations in wind speed, climatological humidity\ngradient, or the anomalous humidity gradient. In particular, over the U.S. East Coast, the increase in the climatological\nhumidity gradient is substantial, yet no quantitative evidence is provided, and only the conclusion that wind anomalies\ndominate is stated. I recommend that the authors conduct a quantitative decomposition using the bulk formula for LHF to\nsupport their interpretation. In addition, including a time series plot (similar to Fig. 4) that illustrates the evolution of LHF\nanomalies and their components (e.g., wind speed, qs – qa) during the development and decay phases of ENSO would help\nelucidate the physical processes for the reader.\nLine 291: How do the authors calculate the SST tendency? Is it estimated based on the mixed-layer heat budget equation, or\ndirectly quantified using the time tendency of mixed-layer SST from model output? Please clarify this in the manuscript.\nLine 300 & Figure 2: I am confused about the relationship between SSTSFC+MLD and SSTTOT. The integration of SFC from MAM to (cid:1) should correspond to the SST anomaly change from spring to (cid:1), i.e., the difference between SSTTOT and SSTMAM. Therefore, in terms of magnitude, this difference may better correspond to the SSTSFC+MLD than SSTTOT alone.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "It is very interesting to see the novel analysis to find the maximum variance of the SST response (maximum R2 value),\ntaking into account the time lag \"tau\", and the distribution of the wind speed response at the time when the ENSO effect is\nmaximized. The finding is also new that the amplitude of influence of ENSO on SSTs increases globally with global\nwarming. On the other hand, the interpretation of the mechanism seems to me to leave some questions unanswered, and I\nfeel that the understanding of the process is not fully developed. The details are described below, but given the number of\nquestionable aspects of the process and the possibility that the way the logic is constructed may need to be fundamentally\nrevised, I would recommend that the paper be rejected and resubmitted after sufficient time for revision.\nMajor points:\n(1) The biggest question is that the pattern of wind speed response considering the lag \"tau\" remains the same whether it is\nEP or CP, and whether the ENSO amplitude is larger or smaller due to global warming (L220-L231, Fig. 3 and Extended\nData Fig. 9). The only possible explanation for the agreement in those figures, even though the teleconnection patterns\nshould clearly be different, is that the lag \"tau\" is different at each location. (Please show the spatial map of lag \"tau\" for EP\nand CP.) It seems to me that the process is no longer about the physics of the ENSO response, but we are simply seeing\nthat the wind speed responses to any forcing from the tropics appears to deviate with large anomalies at the same location at\nthe same timing “tau” that matches the location where the amplitude of the climatological wind speed variation is large.\nPlease plot the spatial map of the annual maxima of wind speed variance on a global map and see if they are consistent\nwith Fig. 3 and Extended Data Fig. 9.\n(2) Regarding the similarity of the spatial pattern of the maximum R2 values in EP and in CP, it appears that the locations\nwhere the maximum R2 values are larger (Fig. 1 and Extended Data Fig. 8) correspond to locations where the climatological\nMLD is shallower (Extended Data Figure 3-a and b). It appears to me that this just means that the shallower the MLD is, the\nmore sensitive it is to atmospheric Q_LH variation. Since the text only discusses the impact of MLD changes due to global\nwarming, I believe that a logical connection is missing.\n(3) The same is true for changes due to global warming: is it possible that the amplitude of the general atmospheric\nteleconnection to any forcing from the tropics is increasing, and the \"increased amplitude of response associated with\nENSO\" referred to in this paper is only a part of what we are seeing?\nIf the above considerations (1) through (3) are true, then the logic construct and story structure of this paper, including the\ntitle, needs to be fundamentally revised. This will take time, which is why I have recommended a rejection and resubmission.\nRequests for additional analyses:\n(4) In the analysis of maximum R2 value, it is not clear which values are related each other and which are independent, so it\nwould help to understand the mechanism if a map of the lag \"tau\" at the maximum absolute value is drawn together. In MME,\nis there uncertainty in the spatial distribution of the lag \"tau\"?\n(5) In the analyses, MAM(0) is used as the baseline and the difference from it is analyzed to judge the maximum R2 value\nand conduct the mixed layer heat budget analysis. But the spatial distribution of the maximum R2 value or the heat budget\nterms must depend not only on the values at the time lag \"tau\" but also on the pattern at MAM(0). How much of impacts do\nthis baseline values have?\n(6) In the Eq.1, not only the SFC term but also the other budget terms (the advection terms, diffusion term) should be shown.\n(7) To understand the differences among MME models in the maximum R2 values and wind speed responses, please add\nfigures of the maximum R2 values and wind speed regressions for each model to the Extended Data Figures.\nMinor comments\n(8) L32-L42: Shifts in the position of the westerly jets due to global warming should also be important in changing the\nteleconnection pattern. Please add this in the text as it is an important factor in understanding the process of this study.\n(9) L81-L82: Is the time lag \"tau\" also similar at each grid point between CESM1 LE and the observations?\n(10) L90-L91: What are the reasons for the weakening of the ENSO impact in the tropical Indian Ocean?\n(11) Please specify the number of ensembles in the CMIP6 1pctCO2 experiments for each model.\n(12) L115: Before the explanation of \"MLD\", the subscript representation of \"SFC+MLD\" appears. At this point, there is no\nexplanation of the difference between \"SFC+MLD\" subscripts, \"SFC\"-only subscripts, and \"MLD\"-only subscripts, which is\nconfusing, so please revise the order of the explanations.\n(13) L284: Why did you use MAM(0) as the baseline? Why were DJF(0) and JFA(0) skipped?\n(14) L484 (Fig. 3 caption): \"g\" -> \"d\"\n(15) L486 (Fig. 3 caption): \"d-e\" -> \"e-f\"\n(16) L487 (Fig. 3 caption): \"f\" -> \"g\"", "reviewer_4": "The manuscript investigates the changes in ENSO-related SST variability under global warming based on climate models. It\nis a very interesting work suggesting that per-unit ENSO SST anomalies could induce amplified global-scale SST feedback\nresponses under global warming scenarios. The results carry significant implications for improving climate projections of\nfuture variability. Through heat budget analysis using model data, the manuscript indicates that intensified ENSO-related\nwind speed and air-sea humidity difference are the main factors contributing to the enhanced SST anomaly responses. To\naddress distinct climate regimes for different regions, the manuscript conducts diagnostics on four selected regions\nexhibiting specific atmospheric feedback responses.\nThe study is well designed and the manuscript is clearly written, guiding the reader towards its main conclusions. The\nmethods section and the Extended file cover necessary details on datasets, analysis and experiments.\nI guess my main comment/question lies in why could the same unit of ENSO SST anomalies lead to a stronger wind\nfeedback through intensified atmospheric convection in the 2100 scenarios, compared with the PD simulation as showed in\nFigure.3. In other words, why could the same magnitude of ENSO SSTA induce stronger atmospheric convection feedback\nin a warming world? Is the cause attributed to changes in the atmospheric mean states affecting convective processes, an\nacceleration in atmosphere feedback establishment, or other factors? I guess a deeper analysis or discussion would greatly\nimprove the comprehension of the underlying physical mechanisms in this manuscript.\nSpecific comments:\n1) I suggest the manuscript could include some analyses on the changes in ENSO amplitude (PD .vs. 2100) from CESM1\nruns. On this basis, the manuscript further examines the global SST variations induced by per unit amplitude of ENSO SSTA\n—even though this does not fundamentally alter the work's main conclusions.\n2) Do any grid points exhibit τ values preceding the ENSO peak (e.g. Atlantic Niño (JJA0) could trigger an ENSO (D0JF1)\nevent)? If present, would this phase-leading relationship necessitate larger-amplitude SSTA forcing to produce equivalent\nENSO SST anomaly intensities?\n3) According to Figure.1, the explainable variance R2 of SST in 2100 simulation is stronger than that in PD. Does this\nfinding suggest a strengthening of the linear response component in ENSO's climatic impacts in 2100 due to more stable\natmospheric stratification?\n4) L33: Suggest using “anomalies” instead of “anomaly”.\n5) There appears to be an inconsistency in Figure 3, where the annotations for panels (e) and (g) seem to be inadvertently\nswapped.\n6) In Figure 4b-e, what does the hatches between the blue and red lines represent?\n7) In Figure 4b-e, unlike the SST results, the temporal evolution of regression coefficients for latent heat flux appears to\nexhibit distinct peak timings (except SIO). Does this suggest an alteration in air-sea interaction processes between PD and\n2100?\n8) There are differences in the chosen time window for τ (L285: JJA(0)-JJA(1); L314: MAM(0)-JJA(1)) in Methods."}, "author_response": "Response to Reviewer #1\nIn this study, the authors explore how greenhouse warming modulates the influence of ENSO\non global SST variability. They show that the ENSO-driven SST variability is expected to\namplify in a warmer climate, primarily due to the increase in El Niño-induced surface wind\nspeed anomaly and the climatological air-sea humidity difference. The study highlights that\nthese changes arise from the strengthened ENSO teleconnections and overall warming of the\nglobal oceans, respectively. The findings provide important new insights into the mechanisms\nthat could intensify regional climate impacts of future El Niño events. Overall, the manuscript\nis well-organized and presents several novel findings. I believe it could be suitable for\npublication after some major revisions.\nWe thank the reviewer for the encouraging and constructive comments. The comments were\nfully considered in the revised manuscript. The point-by-point responses are as follows:\nComments:\n1. Although this manuscript primarily focuses on the impact of unit-strength ENSO events on\nglobal SST variability, it is the overall influence of ENSO on the climate system that is of greater\nconcern, particularly for policy-making and climate risk management. While the authors\nbriefly discuss the effects of ENSO amplitude changes at the end of the manuscript, I suggest\nthat they should present the total change in ENSO’s influence on global SST and quantify the\nrelative contributions of two factors: (a) changes in the sensitivity of SST to unit-strength ENSO\nevents, and (b) changes in ENSO amplitude. Such an analysis would help determine whether\nthe former plays the dominant role in amplifying ENSO’s impact on SSTs. Recent studies (Lin\net al. 2024; Wu et al. 2021) have employed a regression-based method to disentangle the\ncontributions of ENSO amplitude changes and climate sensitivity to ENSO in shaping ENSO’s\ninfluence on regional atmospheric circulation and SST variability. I recommend that the\nauthors refer to these approaches and consider adopting a similar methodology.\nAns) We appreciate this insightful suggestion and fully agree that assessing ENSO’s total\ninfluence on SST is also crucial. In the revision, we quantified ENSO’s overall impact on SST\nas the product of (i) the sensitivity of SST to a unit-strength ENSO event (the regression\n1\ncoefficient, β) and (ii) ENSO amplitude (the standard deviation of the ENSO index, σ).\nFollowing the regression-based decompositions used by Lin et al. (2024) and Wu et al. (2021),\nwe expressed the projected change in ENSO’s SST influence (Δ[βσ]) as the sum of a “non-\namplitude” term (σ·Δβ), an “amplitude” term (β·Δσ), and a small second-order residual\n(Δβ·Δσ). We had added this method to the Extended Data Text.\nDecomposition of changes in El Niño impacts on SSTA\nThe changes in El Niño impacts on SST is calculated as follows:\n𝑌′ = 𝛽×Niño3.4+𝑅𝑒𝑠 , (𝑆1)\n1\nwhere Y' is SSTA(𝜏), β is the regression coefficient, which indicates ENSO-induced SSTA,\nand Res is the residual. Unit of β is K per 1K of DJF Niño3.4 SSTA (DJF Niño3.4). We used\n1\nthe | β | as a representation of ENSO impacts on SSTA.\nFollowing the Lin et al. (2024) and Wu et al. (2021), SSTA(𝜏) regression is also\nconducted with DJF Niño3.4 , which represent standardized Niño3.4 SSTA as follows:\nstd\nNiño3.4 = Niño3.4/𝜎\n𝑠𝑡𝑑\n𝑌′ = 𝛽 ×Niño3.4 +𝑅𝑒𝑠 , (𝑆2)\n𝑠𝑡𝑑 𝑠𝑡𝑑 2\nwhere σ is the standard deviation of DJF Niño3.4, β is the regression coefficient indicating\nstd\nENSO-induced SSTA per one standard deviation of ENSO, and Res is the residual. β\n2 std\nconsists of two influences: (i) non-amplitude factor (the regression coefficient, β) and (ii)\nENSO amplitude factor (the standard deviation of the ENSO index, σ). Since the |β |≈ |β| × σ\nstd\nfrom Eq. S2 and S3, the decomposition of these two factors is calculated as follows:\n∆|𝛽 | = ∆|𝛽|×𝜎 +|𝛽 |×∆𝜎+𝑅𝑒𝑠 , (𝑆3)\n𝑠𝑡𝑑 𝑃𝐷 𝑃𝐷 3\nwhere ∆ means difference between 2100 and PD periods, the subscript PD indicates the PD\nperiod, Res is the residual term. Using Eqs. S2 and S3, we decompose the contributions of\n3\nnon-amplitude and amplitude factor.\nApplying this framework, we found that in CESM1 the increase in ENSO’s SST influence is\nexplained primarily by the non-amplitude component (~64%), with a smaller but non-\nnegligible contribution from amplitude changes (~32%); the remainder is a small cross term.\nIn the CMIP6 multi-model mean, the dominance of the non-amplitude component is even\nclearer (~76%), whereas amplitude changes account for ~20%, with a small residual term.\nsensitivity to a unit ENSO amplitude play a leading role in amplifying ENSO’s overall SST\nimpact, consistent with our main finding that strengthened El Niño-induced wind-speed\nanomalies and enhanced climatological air-sea humidity contrast drive the projected\namplification. The relatively modest contribution of the amplitude factor in the CMIP6 mean\nalso aligns with the interpretation of Extended Data Figure 15.\nWe have revised the Discussion section and added a new Extended Data Figure 14 that maps\nthe total change and its decomposition (non-amplitude vs amplitude).\n(L237-245): To quantify ENSO’s total SST influence, we decomposed it into a non-amplitude\nterm and an amplitude term, following regression-based approaches (Extended Data Text). The\nresults show that the increase is dominated by non-amplitude component changes—~64% in\nCESM1 and ~76% in the CMIP6 multi-model mean—while amplitude accounts for ~32% and\n~20%, respectively; importantly, even models with reduced ENSO amplitude54–56 show a net\nincrease because atmospheric and oceanic sensitivity strengthens (Extended Data Fig. 15 and\n16). In summary, background-state/teleconnection changes dominate, but amplitude changes\nare not negligible.\nLin, S., Dong, B. & Yang, S. Enhanced Impacts of ENSO on the Southeast Asian Summer\nMonsoon Under Global Warming and Associated Mechanisms. Geophysical Research Letters\n51, e2023GL106437 (2024).\nWu, R., Chen, W., Wang, G. & Hu, K. Relative contribution of ENSO and East Asian winter\nmonsoon to the South China Sea SST anomalies during ENSO decaying years. Journal of\nGeophysical Research: Atmospheres 119, 5046–5064 (2014).\nabsolute values of SSTA(𝜏) regressed onto DJF Nino3.4 SSTA from the PD period to 2100.\nThe contribution of b amplitude factor and c non-amplitude factor. d Global mean of (1) the\nchanges in absolute values of SSTA(𝜏) regressed onto DJF Nino3.4 SSTA from the PD period\nto 2100, (2) non-amplitude factor, and (3) amplitude factor. e-h are the same as a-d\n(respectively) but CMIP6 multi-model mean.\n4\n2. The authors attribute the enhanced SST variability associated with ENSO primarily to the\nstrengthening of surface flux anomalies, particularly latent heat flux (LHF). Based on\nqualitative analysis, they argue that the increase in global-mean LHF anomalies is driven by\nboth enhanced climatological air-sea humidity differences and strengthened ENSO-induced\nsurface wind anomalies. However, for several key regions, the analysis lacks sufficient\nquantitative support to convincingly identify the dominant physical drivers. Merely showing\nthe spatial patterns of total LHF and wind anomalies is not adequate to substantiate the claim\n(Lines 201–203) that “the East CS, U.S. East Coast, and SNP are primarily attributed to\nanomalous wind variations, whereas the SIO is influenced by humidity changes.” For instance,\nover the U.S. East Coast, the climatological specific humidity contrast (qs – qa) shows a\nsubstantial increase, which could potentially contribute more to the LHF anomalies than wind\nspeed changes. Similarly, the changes in the SIO region may not be solely governed by the\nincrease in anomalous humidity gradient. I therefore strongly recommend that the authors\nperform a more detailed quantitative analysis using the bulk formula for LHF to examine the\ncontributions from changes in wind speed anomaly, climatological qs – qa, and ENSO-induced\nhumidity gradient anomaly, and other relevant factors. The authors could refer to Lin et al.\n(2024) for relevant diagnostic approaches and analyses.\nAns) To address the reviewer’s concern, we implemented a bulk–aerodynamic decomposition\nof the ENSO-related latent heat flux change from PD to 2100 and then translated latent heat\nflux to SST using our mixed-layer heat budget framework following Lin et al. (2024). And we\nadded this kind of method to the Extended Data Text.\nDecomposition of El Niño-induced latent heat flux anomalies\nWe decompose the changes in El Niño-induced latent heat flux (∆Q' ) following Lin\nLH\net al. (2024), using the bulk formula equation:\n∆𝑄′ ≈ ∆[−𝜌 𝐿𝐶 𝑈(𝑞 −𝑞 )]′\n𝐿𝐻 0 𝑒 𝑠 𝑎\n= −𝜌 𝐿𝐶 ∆[𝑈̅(𝑞 −𝑞 )′]−𝜌 𝐿𝐶 ∆[𝑈′(̅̅ 𝑞 ̅̅̅ − ̅̅̅ 𝑞 ̅̅̅̅)]−𝜌 𝐿𝐶 ∆[𝑈′(𝑞 −𝑞 )′], (𝑆4)\n0 𝑒 𝑠 𝑎 0 𝑒 𝑠 𝑎 0 𝑒 𝑠 𝑎\nwhere ∆ denotes the difference between PD and 2100 period. ρ is air density (1 kg m-3), L is\n0\nlatent heat vaporization (2.5 × 106 J kg-1 ), Ce is the turbulent exchange coefficient (1.4 × 10-\n3), U is the 10 m wind speed, q is the saturation specific humidity at ocean surface, and q is 2\ns a\n5\nm air specific humidity. Since the third term (nonlinear term) of Eq. S4 is negligible, we further\ndecompose the first, and second term as follows:\n−𝜌 𝐿𝐶 ∆[𝑈̅(𝑞 −𝑞 )′] ≈ −𝜌 𝐿𝐶 ∆𝑈̅(𝑞 −𝑞 )′ −𝜌 𝐿𝐶 𝑈̅∆(𝑞 −𝑞 )′ +𝑅 , (𝑆5)\n0 𝑒 𝑠 𝑎 0 𝑒 𝑠 𝑎 0 𝑒 𝑠 𝑎 1\n−𝜌 𝐿𝐶 ∆[𝑈′(̅̅ 𝑞 ̅̅̅ − ̅̅̅ 𝑞 ̅̅̅̅)] ≈ −𝜌 𝐿𝐶 ∆𝑈′(̅̅ 𝑞 ̅̅̅ − ̅̅̅ 𝑞 ̅̅̅̅)−𝜌 𝐿𝐶 𝑈′∆ ̅(̅ 𝑞 ̅̅̅ − ̅̅̅ 𝑞 ̅̅̅̅)+𝑅 , (𝑆6)\n0 𝑒 𝑠 𝑎 0 𝑒 𝑠 𝑎 0 𝑒 𝑠 𝑎 2\nwhere an overbar and a prime (') denote climatology and the regression coefficient onto DJF\nNiño3.4, respectively. R and R are residuals, including each nonlinear term.\n1 2\nThe decomposition is reported, in this order, into four contributions: (1) climatological wind\nspeed, (2) El Niño–induced specific-humidity gradient, (3) El Niño–induced wind speed, and\n(4) climatological specific-humidity gradient, with a small residual for non-linear terms.\nApplied to the four key regions, Figure R1-2 shows time-integrated contributions of the\ndecomposed terms from MAM(0) to 𝜏 (timing of maximum changes in R2 values from\nMAM(0), see Main text methods for details). This reveals that the El Niño-induced wind\nspeed contribution is the dominant amplifier outside the South Indian Ocean (SIO): it explains\n~30% of the net change in the East China Sea (EastCS), ~72% along the U.S. East Coast, and\n~119% in the Subtropical North Pacific (SNP; exceeding 100% because it offsets a damping\nof ENSO-induced humidity difference term).\nIn the SIO, by contrast, the leading contribution is the increase in the climatological specific-\nhumidity gradient (~54%), consistent with enlarged climatological specific humidity gradient;\nthe El Niño–driven humidity-gradient anomaly provides an additional ~33% (Extended Data\nFig. 7). Two secondary patterns refine our interpretation: the El Niño–induced specific-\nhumidity gradient provides an additional positive contribution in the EastCS (~65%), whereas\nit damps the response in the SNP (–38%). Although the ENSO-driven anomaly in the air–sea\nspecific-humidity difference, Δ(q –q )′, can be quantitatively important at regional scales, it\ns a\ncannot, by itself, account for the global amplification of ENSO-induced SST variability. Finally,\nthe increase in the climatological air–sea humidity contrast contributes to ENSO-induced SSTA\nacross all four regions and therefore qualitatively contributes to the amplification of ENSO-\ninduced SST variability, even where its quantitative role is secondary (outside the SIO).\nTaken together, these diagnostics corroborate our statement that strengthened winds are the\nprevailing driver of the amplified ENSO-SST variability in most regions, while background\nhumidity increases are most important in the SIO. We have integrated these results into the\n6\nrevised Results section and Extended Data Text. Some elements here overlap with the\nfollowing specific comments, the Lines 186–203, please cross-reference both sections. Here,\nδq indicates q –q .\ns a\n(L189-208): While Figures 2 and 3 provide a global overview of sensitivity changes, a regional\nperspective reveals that El Niño–induced SST anomalies intensify under warming across four\nrepresentative basins—the East China Sea (EastCS), the U.S. East Coast, the South Indian\nOcean (SIO), and the subtropical North Pacific (SNP)—with the amplification primarily driven\nby ENSO-induced latent heat flux changes (ΔQ' ) (Fig. 4; Extended Data Fig. 7). In the\nLH\nEastCS, an intensified western North Pacific anticyclone during D(0)JF(1)–MAM(1) weakens\nthe climatological northwesterlies and reduces evaporation, yielding warmer SSTA (Fig. 4b;\nExtended Data Fig. 8 and 9). Along the U.S. East Coast, stronger ENSO-induced westerlies\nfrom ASO(0) to JFM(1) increase wind speed and enhance local evaporation, producing\npronounced cooling that peaks in FMA(1) (Fig. 4c; Extended Data Fig. 8, and 9). In the SIO,\nby contrast, evaporation decreases and warmer SSTAs develop (Fig. 4d), primarily governed\nby increases in climatological δq rather than by ENSO-induced wind speed (Extended Data\nFig. 9 and 11). The SNP experiences cooler SST (Fig. 4e), as strengthened ENSO-induced\nwinds align with the trade winds from ND(0)J(1) to AMJ(1), increasing wind speed and\nevaporation; here, ENSO-induced wind speed changes lead, whereas ENSO-induced δq acts as\na damping (Extended Data Fig. 8-10). Across regions, MLD changes are secondary.\nCollectively, these results indicate that changes in the background δq—which modulate the\nefficacy of latent heat fluxes—together with stronger, region-dependent ENSO-induced wind\nanomalies, lead to an overall amplification of El Niño’s SST impacts (see Extended Data Text\nfor quantitative shares and seasonal phasing).\n(Extended Data Text, L49-95):\nRegional mechanisms behind the changes in ENSO-induced latent heat flux\nTo illuminate the physical pathways underlying the regional amplification of El Niño–induced\nSST anomalies, we examine four representative basins—the East China Sea (EastCS), the U.S.\nEast Coast, the South Indian Ocean (SIO), and the subtropical North Pacific (SNP)—where\nimpacts are evident in the PD simulation and intensify under warming (Fig. 4). Because we\nfound that the changes in ENSO-induced SSTA of all the four regions are dominated by ENSO-\ninduced latent heat flux (∆Q' , Extended Data Fig. 7; EastCS ~65%, U.S. East Coast ~81%,\nLH\nSIO ~124%, and SNP ~59%), we further decompose the ∆Q' : the changes in climatological\nLH\nwind speed, climatological δq, ENSO-induced wind speed anomalies, and ENSO-induced δq,\nfollowing the bulk-aerodynamic framework of Lin et al. (2024).\nIn the EastCS, during El Niño, particularly from D(0)JF(1) to MAM(1), an intensified\nanomalous western North Pacific anticyclone extends further northeastward and weakens\nclimatological northwesterly (Extended Data Figs. 8a, i and 10). This weakening reinforces the\nincrease in ∆Q' (downward positive, i.e., reduced evaporation) via two pathways: (i) a larger\nLH\nnegative wind speed anomaly, and (ii) a larger positive ENSO-induced q anomaly that reduces\na\nδq. Both effects suppress evaporation and thus promote stronger warm SSTA, peaking in\ninfluenced by enhanced ENSO-induced wind speed (~72%) and climatological δq (~26%)\nvariations (Extended Data Fig. 9b). From ASO(0) to JFM(1), stronger westerly winds enhance\nlocal evaporation (Extended Data Figs. 8b, f, 10b, j), resulting in significant cooling and a cold\nSSTA that peaks in FMA(1).\nIn the SIO, ∆Q' accounts for 124% of the warm ∆SST because they are partially\nLH SFC\ncounterbalanced by reduced shortwave heating (∆Q' ≈ -88%) associated with cloud47, which\nSW\nis in turn largely offset by increased longwave heating (∆Q' ≈ +60%). Within ∆Q' , humidity\nLW LH\ndifference effects dominate (climatological ~54%; ENSO-induced ~33%), whereas the ENSO-\ninduced wind speed term is net negative (~ -13%; Extended Data Fig.9c). The SIO shows\nclimatological easterlies; during ENSO development, the resulting weak northeasterly\nanomalies (Extended Data Fig. 11) advect moister air into the region, suppressing evaporation,\nand reinforcing the warm SSTA while the wind speed anomalies remain weak negative. In the\ndecay phase, ENSO-induced westerlies oppose the climatological easterlies and reduce wind\nspeed, while warm SSTA raises ENSO-induced δq, which acts as a cooling contribution\n(Extended Data Fig. 8). Even so, the combined effect remains positive in both phases, and the\nchanges in climatological δq always acts as a positive effect to increase in ∆Q' .\nLH\nThe SNP experiences cooler SSTA in future climate, driven by ∆Q' , especially the changes\nLH\nin ENSO-induced wind speed (~119%), while the changes in ENSO-induced δq act as a\ndamping (~ -38%, Extended Data Fig. 9). Because the change in ENSO-induced wind from\nND(0)J(1) to AMJ(1) not only becomes stronger but also aligns with the trade wind,\nsignificantly increasing wind speed in SNP (Extended Data Fig. 7, 8, and 9). In contrast, ENSO-\ninduced δq damps the SST, because much cooler SSTA reduces q . Across regions, the changes\ns\nin MLD have a relatively larger contribution in the SIO and SNP compared to the EastCS and\nU.S. East Coast, yet the contributions are insignificant across all regions. Overall, each region\nexhibits distinct seasonal characteristics and dominant contributing factors: the U.S. East Coast,\nand SNP are dominated by ENSO-induced wind changes, whereas the EastCS and SIO are\nshaped mainly by ENSO-induced and climatological δq, respectively. Although ENSO-induced\nδq can be sizable locally, only the climatological humidity difference consistently contributes\npositively across all four regions, indicating that the global-scale enhancement of ENSO-SST\nvariability is not driven by ENSO-induced δq but by robust increases in the climatological δq.\nLin, S., Dong, B. & Yang, S. Enhanced Impacts of ENSO on the Southeast Asian Summer\nMonsoon Under Global Warming and Associated Mechanisms. Geophysical Research Letters\n51, e2023GL106437 (2024).\nSST in four key regions. Panels show four regions: a East China Sea (125°E–145°E, 20°N–\n35°N), b U.S. east coast (60°W–80°W, 25°N–40°N), c South Indian Ocean (50°E–90°E, 40°S–\n20°S), and d the subtropical North Pacific (150°E–165°W, 12°N–22°N). Bars quantify the PD\nto 2100 change in the contribution to ENSO-related latent heat flux-induced SST;\nclimatological wind speed (green), El Niño–induced specific-humidity gradient (blue), El\nNiño–induced wind speed (yellow), and climatological specific-humidity gradient (purple);\nthe gray bar is the residual. Error bars denote 95% confidence intervals from bootstrap test.\n9\n3. It is well known that El Niño and La Niña are asymmetric in terms of the center of SSTA,\namplitude, persistence, and other characteristics, leading to their distinct and asymmetric\nimpacts on the global climate. However, the current analysis is primarily based on correlation\nor regression methods, which implicitly assume linearity and overlook the influence of ENSO\nasymmetry. Therefore, I think that the authors should examine the composite changes in global\nSST separately for El Niño and La Niña events. At the very least, they should acknowledge this\nlimitation and provide a brief discussion of its potential implications for their results.\nAns) To assess this explicitly, we constructed separate composites of global SST anomalies for\nEl Niño and La Niña events for the PD and for 2100, and we further examined the change in\ntheir absolute values (|2100|–|PD|; Fig. R1-3). Events are defined by DJF Niño-3.4 SSTA\nexceeding ±1 standard deviation (≥+1σ for El Niño; ≤−1σ for La Niña), and the temporal\nalignment follows the same protocol as the maximum R² analysis (Methods). Here, however,\nwe analyze composite amplitudes rather than correlations.\nThe composites reproduce the canonical asymmetries between El Niño and La Niña in\nteleconnections and show that the projected changes are themselves asymmetric. For El Niño,\nthe SST response strengthens over most ocean basins, broadly consistent with our linear\nregression results. For La Niña, although the global‐mean amplitude also increases, the local\npattern of change differs: the influence weakens over the Kuroshio–Oyashio Extension east of\nJapan, while it strengthens in several regions—including the North Pacific, the South Pacific\nConvergence Zone, and the Maritime Continent—in some cases exceeding the corresponding\nEl Niño enhancement. We acknowledge this El Niño–La Niña teleconnection asymmetry and\nhave revised the Discussion section to explicitly note this point.\n(L230-234): Additionally, to address the ENSO asymmetry, we further examined El Niño and\nLa Niña composites separately; in both cases, the SST response increases under warming, yet\nthe regional patterns are asymmetric. Although a more detailed analysis is needed to fully\ninterpret the asymmetries, it is clear that the SST impacts in both El Nino and La Nina cases\nincrease.\nPD, 2100, and the change in absolute magnitude (|2100|-|PD|), taking the absolute maximum\nover the JJA(0)–JJA(1). d-f As in a-c but for La Niña. Stippling denotes regions where the\nchange in absolute amplitude is statistically significant at the 95% confidence level using a\nbootstrap test. The white box marks the equatorial Pacific region to exclude ENSO SST itself.\n11\n4. I noticed that the equatorial Indian Ocean exhibits a significant decrease in ENSO-induced\nSST variability. However, both surface wind speed and the climatological humidity gradient in\nthis region are projected to strengthen under global warming, which suggests that changes in\nLHF anomalies alone may not fully explain the reduced SST variability there. Do the authors\nhave a plausible explanation for this contrasting behavior? What mechanisms might be\nresponsible for the weakened ENSO-induced SST variability in this region, especially given the\noverall strengthening of ENSO impacts observed elsewhere?\nAns) Thanks for asking an important question. Figure R1-4 examines the equatorial Indian\nOcean and contrasts four diagnostics—SST , SST , SST , and SST —together\nTOT SFC+MLD SFC MLD\nwith time series of SST regressed onto the DJF Niño3.4 SSTA. The gap between SST and\nTOT\nSST indicates a substantial contribution from ocean dynamics in this region. Consistent\nSFC+MLD\nwith previous studies, El Niño is linked to Indian Ocean variability through two phases: during\nthe growth phase, as well as the atmospheric heat flux, dynamical processes (thermocline\nshoaling/upwelling and associated circulation changes) promote a positive Bjerknes feedback\n(Bjerknes, 1969) fosters development of the Indian Ocean Dipole (IOD) and enhance cooling\nin the eastern equatorial basin (Saji et al., 1999); during the decay phase, surface heat-flux\nanomalies favor the Indian Ocean Basin Mode (IOBM) and yield basin-wide warming (Cai et\nal, 2019 and Klein et al, 1999).\nBecause our primary decomposition isolates the surface flux pathway, the growing-phase\ndynamical cooling is not fully captured, implying that our thermodynamic estimate is not\nadequate to fully explain SST evolution. This interpretation is supported by the diagnostics:\nSST shows stronger cooling than SST , consistent with an enhanced dynamical\nTOT SFC+MLD\ncontribution, whereas SST indicates that the surface flux pathway itself strengthens under\nSFC\nwarming. Taken together, these results suggest that the overall El Niño influence on the\nequatorial Indian Ocean intensifies, but the decaying-phase warm SST anomaly becomes\nweaker in 2100. This attenuation explains why the signal over the equatorial Indian Ocean\nappears muted (and locally cooler) despite the increased ENSO impact.\nThe time series shows El Niño’s stronger impacts on Indian Ocean cooling in SON(0) and\nweaker impacts on Indian Ocean warming in D(0)JF(1) to JJA(1).\nMonthly Weather Review 97, 163–172 (1969).\nCai, W. et al. Pantropical climate interactions. Science 363, eaav4236 (2019).\nSaji, N. H., Goswami, B. N., Vinayachandran, P. N. & Yamagata, T. A dipole mode in the\ntropical Indian Ocean. Nature 401, 360–363 (1999).\nKlein, S. A., Soden, B. J. & Lau, N.-C. Remote Sea Surface Temperature Variations during\nENSO: Evidence for a Tropical Atmospheric Bridge. (1999).\nFig. R1-4 | The mechanisms of decrease in El Niño impact on Indian Ocean SST\nvariability. a Same as Extended Fig. 5a for Equatorial Indian Ocean (60°E–85°E, 10°S–5°N).\nb Temporal evolution of SSTA regressed onto DJF Niño3.4 SSTA from MAM(0) to JJA(1),\nduring PD (blue line) and the 2100 period (red line) for the equatorial Indian Ocean. c Temporal\nevolution of SSTA changed by the only El Niño-induced surface flux (conducted with SFC\nregressed on DJF Niño3.4 SSTA). Red dots indicate statistically significant at the 95%\nconfidence level using the bootstrap test.\nLines 40–42: Consider citing some references here to support the statement, such as recent\nstudies on the enhanced impact of ENSO on East Asian climate under global warming (e.g.,\nLin et al. 2024).\nAns) We have added recent studies such as Lin et al. 2024, and Cai et al. 2020.\nLin, S., Dong, B. & Yang, S. Enhanced Impacts of ENSO on the Southeast Asian Summer\nMonsoon Under Global Warming and Associated Mechanisms. Geophysical Research Letters\n51, e2023GL106437 (2024).\nCai, W. et al. Climate impacts of the El Niño–Southern Oscillation on South America. Nat Rev\nEarth Environ 1, 215–231 (2020).\nLines 57–59: I also suggest citing more recent literature to support this point.\nAns) We have added recent studies (Power et al. 2013, Yun et al. 2021, Zhou et al. 2014, and\nHu et al. 2021).\nPower, S., Delage, F., Chung, C., Kociuba, G. & Keay, K. Robust twenty-first-century\nprojections of El Niño and related precipitation variability. Nature 502, 541–545 (2013).\nYun, K.-S. et al. Increasing ENSO–rainfall variability due to changes in future tropical\ntemperature–rainfall relationship. Commun Earth Environ 2, 1–7 (2021).\nZhou, Z.-Q., Xie, S.-P., Zheng, X.-T., Liu, Q. & Wang, H. Global Warming–Induced Changes\nin El Niño Teleconnections over the North Pacific and North America. Journal of Climate 27,\n9050–9064 (2014).\nHu, K., Huang, G., Huang, P., Kosaka, Y. & Xie, S.-P. Intensification of El Niño-induced\natmospheric anomalies under greenhouse warming. Nat. Geosci. 14, 377–382 (2021).\nLine 72: It is unclear whether this refers to a 3-month mean SSTA or a 3-month running mean\nSSTA. This should be clarified, as the method section (Line 281) seems to indicate the latter.\nAns) Revised the manuscript to “3-month running mean SSTA”\nincreased SFC are dominated by longwave radiation induced by ENSO. Please add the\ndifference between the 2100 and PD to help support this claim.\nAns) Thank you for the helpful suggestion. We now explicitly quantify the PD to 2100\ndifference. Figure R1-5e shows the global mean of the shortwave and longwave radiative\ncontributions to the SST (Fig. R1-5 and Extended Data Fig. 5).\nSFC\nlongwave radiation to global SST. a Shortwave radiation contribution to SSTSFC+MLD\nduring PD period in CESM1. b Same as a but during 2100. c-d Same as a and b, but longwave\nradiation. e Global mean of shortwave radiation contribution (left) and longwave radiation\ncontribution (right) in PD and 2100 period. Error bars are calculated at the 95% confidence\nlevel using a bootstrap test.\nLine 144: Consider changing “moisture anomalies” to “mean moisture,” if referring to the\nbackground state.\nAns) Revised to “mean moisture”\nLines 165–166: Do you mean the mean or the anomaly of the specific humidity difference (qs\n– qa)? I believe the intended meaning may be the change in the climatological humidity\ngradient. Please clarify.\nExtended Data Fig. 5: The labels are too small, making the figure difficult to read. The authors\nmay want to adjust the panel layout to enlarge each subplot and improve legibility.\nAns) We adjusted the panel layout of Extended Data Fig. 5 (Now, Extended Data Fig. 7; Fig.\nR1-6).\nregion. a, b, e and f Same as Fig. 2b but the integrated term before multiplied by the sign of\nSST (∆SST , ∆SST , and ∆SST ) and ∆SST is not absolute value (see Methods\nTOT SFC+MLD SFC MLD TOT\nfor details) for each region: a East China Sea (125°E–145°E, 20°N–35°N), b U.S. east coast\n(60°W–80°W, 25°N–40°N), e South Indian Ocean (50°E–90°E, 40°S–20°S), and f the\nsubtropical North Pacific (150°E–165°W, 12°N–22°N). c, d, g, and h The same as Fig. 2c, but\nfor each region: c East China Sea, d U.S. east coast, g South Indian Ocean, and h the subtropical\nNorth Pacific rather than global mean.\nspeed, climatological humidity gradient, or the anomalous humidity gradient. In particular,\nover the U.S. East Coast, the increase in the climatological humidity gradient is substantial,\nyet no quantitative evidence is provided, and only the conclusion that wind anomalies dominate\nis stated. I recommend that the authors conduct a quantitative decomposition using the bulk\nformula for LHF to support their interpretation. In addition, including a time series plot\n(similar to Fig. 4) that illustrates the evolution of LHF anomalies and their components (e.g.,\nwind speed, qs – qa) during the development and decay phases of ENSO would help\nelucidate the physical processes for the reader.\nAns) Following the reviewer’s suggestion, we quantitatively decomposed the change in ENSO-\nrelated latent heat flux anomalies from PD to 2100 using the bulk aerodynamic formula and\nexamined the seasonal evolution of each term (Fig. R1-7). Across all four regions, the\ncontribution from ENSO-induced wind-speed anomalies is substantial and generally dominant.\nIn the East China Sea, the ENSO-driven humidity-gradient anomaly makes an additional\nsizeable contribution during D(0)JF(1)–MAM(0). Along the U.S. East Coast, the results most\nclosely follow our main mechanism: the strengthened El Niño wind-speed anomaly is the\nleading amplifier, while the increased climatological air–sea humidity contrast provides a\nsecondary positive effect. In the South Indian Ocean, the humidity-gradient anomaly\ncontributes to warming during the development phase, whereas from D(0)JF(1) the decrease in\nwind speed becomes the main positive (warming) contributor; over the same period the ENSO-\ndriven humidity-gradient anomaly acts as a damping term, while the climatological humidity\ngradient contributes positively throughout. In the Subtropical North Pacific, enhanced El Niño\nwind-speed anomalies again dominate, and the ENSO-driven humidity-gradient anomaly\ndamps the response, consistent with larger SST-driven humidity changes there.\n(L189-208): While Figures 2 and 3 provide a global overview of sensitivity changes, a regional\nperspective reveals that El Niño–induced SST anomalies intensify under warming across four\nrepresentative basins—the East China Sea (EastCS), the U.S. East Coast, the South Indian\nOcean (SIO), and the subtropical North Pacific (SNP)—with the amplification primarily driven\nby ENSO-induced latent heat flux changes (ΔQ' ) (Fig. 4; Extended Data Fig. 7). In the\nLH\nEastCS, an intensified western North Pacific anticyclone during D(0)JF(1)–MAM(1) weakens\nthe climatological northwesterlies and reduces evaporation, yielding warmer SSTA (Fig. 4b;\nfrom ASO(0) to JFM(1) increase wind speed and enhance local evaporation, producing\npronounced cooling that peaks in FMA(1) (Fig. 4c; Extended Data Fig. 8, and 9). In the SIO,\nby contrast, evaporation decreases and warmer SSTAs develop (Fig. 4d), primarily governed\nby increases in climatological δq rather than by ENSO-induced wind speed (Extended Data\nFig. 9 and 11). The SNP experiences cooler SST (Fig. 4e), as strengthened ENSO-induced\nwinds align with the trade winds from ND(0)J(1) to AMJ(1), increasing wind speed and\nevaporation; here, ENSO-induced wind speed changes lead, whereas ENSO-induced δq acts as\na damping (Extended Data Fig. 8-10). Across regions, MLD changes are secondary.\nCollectively, these results indicate that changes in the background δq—which modulate the\nefficacy of latent heat fluxes—together with stronger, region-dependent ENSO-induced wind\nanomalies, lead to an overall amplification of El Niño’s SST impacts (see Extended Data Text\nfor quantitative shares and seasonal phasing).\n(Extended Data Text, L49-95):\nRegional mechanisms behind the changes in ENSO-induced latent heat flux\nTo illuminate the physical pathways underlying the regional amplification of El Niño–induced\nSST anomalies, we examine four representative basins—the East China Sea (EastCS), the U.S.\nEast Coast, the South Indian Ocean (SIO), and the subtropical North Pacific (SNP)—where\nimpacts are evident in the PD simulation and intensify under warming (Fig. 4). Because we\nfound that the changes in ENSO-induced SSTA of all the four regions are dominated by ENSO-\ninduced latent heat flux (∆Q' , Extended Data Fig. 7; EastCS ~65%, U.S. East Coast ~81%,\nLH\nSIO ~124%, and SNP ~59%), we further decompose the ∆Q' : the changes in climatological\nLH\nwind speed, climatological δq, ENSO-induced wind speed anomalies, and ENSO-induced δq,\nfollowing the bulk-aerodynamic framework of Lin et al. (2024).\nIn the EastCS, during El Niño, particularly from D(0)JF(1) to MAM(1), an intensified\nanomalous western North Pacific anticyclone extends further northeastward and weakens\nclimatological northwesterly (Extended Data Figs. 8a, i and 10). This weakening reinforces the\nincrease in ∆Q' (downward positive, i.e., reduced evaporation) via two pathways: (i) a larger\nLH\nnegative wind speed anomaly, and (ii) a larger positive ENSO-induced q anomaly that reduces\na\nδq. Both effects suppress evaporation and thus promote stronger warm SSTA, peaking in\ninfluenced by enhanced ENSO-induced wind speed (~72%) and climatological δq (~26%)\nvariations (Extended Data Fig. 9b). From ASO(0) to JFM(1), stronger westerly winds enhance\nlocal evaporation (Extended Data Figs. 8b, f, 10b, j), resulting in significant cooling and a cold\nSSTA that peaks in FMA(1).\nIn the SIO, ∆Q' account for 124% of the warm ∆SST because they are partially\nLH SFC\ncounterbalanced by reduced shortwave heating (∆Q' ≈ -88%) associated with cloud47, which\nSW\nis in turn largely offset by increased longwave heating (∆Q' ≈ +60%). Within ∆Q' , humidity\nLW LH\ndifference effects dominate (climatological ~54%; ENSO-induced ~33%), whereas the ENSO-\ninduced wind speed term is net negative (~ -13%; Extended Data Fig.9c). The SIO shows\nclimatological easterlies; during ENSO development, the resulting weak northeasterly\nanomalies (Extended Data Fig. 11) advect moister air into the region, suppressing evaporation,\nand reinforcing the warm SSTA while the wind speed anomalies remain weak negative. In the\ndecay phase, ENSO-induced westerlies oppose the climatological easterlies and reduce wind\nspeed, while warm SSTA raises ENSO-induced δq, which acts as a cooling contribution\n(Extended Data Fig. 8). Even so, the combined effect remains positive in both phases, and the\nchanges in climatological δq always acts as a positive effect to increase in ∆Q' .\nLH\nThe SNP experiences cooler SSTA in future climate, driven by ∆Q' , especially the changes\nLH\nin ENSO-induced wind speed (~119%), while the changes in ENSO-induced δq acts as a\ndamping (~ -38%, Extended Data Fig. 9). Because the change in ENSO-induced wind from\nND(0)J(1) to AMJ(1) not only becomes stronger but aligns with the trade wind,\nsignificantly increasing wind speed in SNP (Extended Data Fig. 7, 8 and 9). In contrast, ENSO-\ninduced δq damps the SST, because much cooler SSTA reduces q . Across regions, the changes\ns\nin MLD have a relatively larger contribution in the SIO and SNP compared to the EastCS and\nU.S. East Coast, yet the contributions are insignificant across all regions. Overall, each region\nexhibits distinct seasonal characteristics and dominant contributing factors: the U.S. East Coast,\nand SNP are dominated by ENSO-induced wind changes, whereas the EastCS and SIO are\nshaped mainly by ENSO-induced and climatological δq, respectively. Although ENSO-induced\nδq can be sizable locally, only the climatological humidity difference contributes consistently\npositively across all four regions, indicating that the global-scale enhancement of ENSO-SST\nvariability is not driven by ENSO-induced δq but by robust increases in the climatological δq.\nof the change in ENSO-related latent heat flux anomalies from PD to 2100 and its components\nfor four regions: a-d East China Sea, U.S. East Coast, South Indian Ocean, and Subtropical\nNorth Pacific. The top row shows the total latent heat flux change (black) together with the\nreconstructed sum of components (gray dashed line). Red, blue, and green dashed line are each\nterm of Eq. S4 (Extended Data for details). e-h The middle row separates the two terms that\ncontain the ENSO-driven humidity-gradient anomaly (solid line), and the change in the\nclimatological wind speed (dashed line). i-l The bottom row separates the two terms that\ncontain the ENSO-induced wind-speed (solid curve) and the change in the climatological\nhumidity difference (dashed line).\nLine 291: How do the authors calculate the SST tendency? Is it estimated based on the mixed-\nlayer heat budget equation, or directly quantified using the time tendency of mixed-layer SST\nfrom model output? Please clarify this in the manuscript.\nAns) We did not diagnose the model’s full mixed-layer temperature tendency ∂T /∂t nor close\nm\nthe complete budget. Instead, we used the mixed-layer heat budget as a diagnostic framework\nand quantified only the surface flux-driven component of the ENSO-related SSTA by time-\nintegrating the surface flux term:\n22\n𝜏 𝑄′\n𝑛𝑒𝑡\n𝑆𝑆𝑇 = 𝑠𝑖𝑔𝑛(𝑆𝑆𝑇 )×∫ 𝑑𝑡\n𝑆𝐹𝐶+𝑀𝐿𝐷 𝑇𝑂𝑇 𝜌 𝐶 ℎ̅\n𝑀𝐴𝑀(0) 0 𝑝\n𝜏 𝑄′\n𝑆𝑆𝑇 = 𝑠𝑖𝑔𝑛(𝑆𝑆𝑇 )×∫ 𝑛𝑒𝑡 𝑑𝑡\n𝑆𝐹𝐶 𝑇𝑂𝑇 𝜌 𝐶 ℎ̅\n𝑀𝐴𝑀(0) 0 𝑝 𝑃𝐷\n𝜏 𝑄′\n𝑛𝑒𝑡,𝑃𝐷\n𝑆𝑆𝑇 = 𝑠𝑖𝑔𝑛(𝑆𝑆𝑇 )×∫ 𝑑𝑡\n𝑀𝐿𝐷 𝑇𝑂𝑇 𝜌 𝐶 ℎ̅\n𝑀𝐴𝑀(0) 0 𝑝\nHere, Q’net is the regression coefficient of net surface flux with respect to DJF Niño3.4 SSTA,\nℎ̅ is the climatological mixed layer depth to calculate SST\nSFC+MLD\n. We further isolate effects as\nSST\nSFC\nand SST\nMLD\nso that changes in ℎ̅ and in Q’net are separated. The residual (SST\nTOT\n-\nSST ) collects all non-surface flux processes such as horizontal/vertical advection,\nSFC+MLD\nentrainment process, and diffusion. We have revised the Methods to explicitly state this and\nreplaced phrases suggesting “SST tendency” with wording “surface flux-induced SSTA”. We\nhave also clarified this point in the revised manuscript.\n(L306-313): We calculated the surface flux-induced SSTA in diagnostic framework, based on\nthe following mixed layer heat budget equation:\n𝜕𝑇 𝑄\n𝑚 𝑛𝑒𝑡\n= +𝑅𝑒𝑠, (1)\n𝜕𝑡 𝜌 𝐶 ℎ\n0 𝑝\nwhere T is the mixed layer temperature, Q is net surface flux (positive into the ocean), 𝜌\nm net 0\n(1027 kg m-3) is the reference density of seawater, C (3985 J kg-1 K-1) is the specific heat of\np\nseawater at constant pressure. We only used surface flux term and calculate the surface flux\ncontribution to ENSO-driven SSTA. The Res mainly represents contributions from ocean\ndynamical processes such as horizontal/vertical advection, entrainment, and diffusion.\nLine 300 & Figure 2: I am confused about the relationship between SSTSFC+MLD and\nSSTTOT. The integration of SFC from MAM to 𝜏 should correspond to the SST anomaly change\nfrom spring to 𝜏, i.e., the difference between SSTTOT and SSTMAM. Therefore, in terms of\nmagnitude, this difference may better correspond to the SSTSFC+MLD than SSTTOT alone.\ncontribution (SST ) is obtained by integrating from MAM(0) to 𝜏 and should\nSFC+MLD\ntherefore be compared with the change in SST over the same interval. While Figure 4\nfollowed this convention, we inadvertently compared SST with SST (𝜏) itself in\nSFC+MLD TOT\nFigure 2 and Extended Data Figures 2, 5, and 10. We have corrected all four figures to use\nthe MAM(0) to 𝜏 change, which reduces the residual and improves model consistency. In\nresponse to the reviewer’s comment, we have revised the Methods section as follows:\n(L318-319): SST is the difference between the regression coefficient of SSTA with\nTOT\nrespect to DJF Niño3.4 SSTA at time 𝜏 and MAM(0)\nReviewers #2\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nReviewers #3\nIt is very interesting to see the novel analysis to find the maximum variance of the SST response\n(maximum R2 value), taking into account the time lag \"tau\", and the distribution of the wind\nspeed response at the time when the ENSO effect is maximized. The finding is also new that the\namplitude of influence of ENSO on SSTs increases globally with global warming. On the other\nhand, the interpretation of the mechanism seems to me to leave some questions unanswered,\nand I feel that the understanding of the process is not fully developed. The details are described\nbelow, but given the number of questionable aspects of the process and the possibility that the\nway the logic is constructed may need to be fundamentally revised, I would recommend that\nthe paper be rejected and resubmitted after sufficient time for revision.\nWe would like to thank the reviewers for their careful and constructive suggestions, which\nhelped to improve the quality of this manuscript. Our responses to the specific comments are\nas follows:\nComments:\n1. The biggest question is that the pattern of wind speed response considering the lag \"tau\"\nremains the same whether it is EP or CP, and whether the ENSO amplitude is larger or smaller\ndue to global warming (L220-L231, Fig. 3 and Extended Data Fig. 9). The only possible\nexplanation for the agreement in those figures, even though the teleconnection patterns should\nclearly be different, is that the lag \"tau\" is different at each location. (Please show the spatial\nmap of lag \"tau\" for EP and CP.) It seems to me that the process is no longer about the physics\nof the ENSO response, but we are simply seeing that the wind speed responses to any forcing\n25\nfrom the tropics appears to deviate with large anomalies at the same location at the same timing\n“tau” that matches the location where the amplitude of the climatological wind speed variation\nis large. Please plot the spatial map of the annual maxima of wind speed variance on a global\nmap and see if they are consistent with Fig. 3 and Extended Data Fig. 9.\nAns) Thank you for the important questions. As the reviewer noted, El Niño teleconnections\ncan vary depending on ENSO pattern diversity, amplitude, phase (El Niño versus La Niña),\nand many other factors. The intention of our study is to highlight changes in ENSO impacts in\nan averaged sense across whole events. The observational global SST anomalies shown in\nExtended Data Fig. 1 exhibit the well-known pattern that has been consistently explained in\nprevious literature, even though various ENSO types are mixed. Our model reproduces this\nobserved pattern well, suggesting that our approach captures the underlying physical processes.\nAs the reviewer suggested, we now provide spatial maps of the lag τ that maximizes the 10 m\nwind speed regression onto Niño3.4, Niño3 (EP), and Niño4 (CP) SSTAs (Fig. R2-1a-c). The\nphase of the wind speed response is largely insensitive to the ENSO index used: EP and CP\nmaps closely resemble the Niño3.4 result, and many regions (stippling) share the same τ.\nComparison with the calendar month of the 10 m wind speed variance maximum shows\ncoincidence over only ~20% of the ocean area (Fig. R2-1d). Therefore, the ENSO-related wind\nspeed patterns do not simply mirror where the climatological wind variance happens to peak.\nIt reflects the seasonally phased teleconnection.\nmaximum. a-c, Calendar month of the maximum regression of 10 m wind speed onto Niño3.4,\nNiño3, and Niño4 SSTA. Stippling marks grid points where all three indices yield the same\npeak month. d Calendar month of the standard deviation, maximum of 3-month running mean\n10 m wind speed; stippling indicates grid points where this month coincides with the ENSO\npeak month.\n27\n2. Regarding the similarity of the spatial pattern of the maximum R2 values in EP and in CP, it\nappears that the locations where the maximum R2 values are larger (Fig. 1 and Extended Data\nFig. 8) correspond to locations where the climatological MLD is shallower (Extended Data\nFigure 3-a and b). It appears to me that this just means that the shallower the MLD is, the more\nsensitive it is to atmospheric Q_LH variation. Since the text only discusses the impact of MLD\nchanges due to global warming, I believe that a logical connection is missing.\nAns) We thank the reviewer for this suggestion. The pattern correlation between the Present-\nday (PD) climatological mixed layer depth (MLD) and the peak R2 is r = -0.05; the pattern\ncorrelation between PD climatological MLD and the change in peak R2 is r = 0.02. Both values\nare nearly zero, indicating that regions of large peak R2 and regions where peak R2 increases\nare not systematically co-located with shallow MLD.\nWe agree with the physical intuition that, all else equal, a shallower mixed layer can heighten\nlocal SST sensitivity to surface fluxes. However, in our results the spatial structure of where\nENSO explains the most variance—and where that influence strengthens—does not track PD\nMLD. This implies that MLD is at most a secondary modulator, and that other factors tied to\nENSO forcing and air-sea coupling organize the pattern more strongly.\n3. The same is true for changes due to global warming: is it possible that the amplitude of the\ngeneral atmospheric teleconnection to any forcing from the tropics is increasing, and the\n\"increased amplitude of response associated with ENSO\" referred to in this paper is only a part\nof what we are seeing? If the above considerations (1) through (3) are true, then the logic\nconstruct and story structure of this paper, including the title, needs to be fundamentally revised.\nThis will take time, which is why I have recommended a rejection and resubmission.\nAns) We sincerely appreciate the reviewer’s insightful point that atmospheric teleconnections\nfrom the tropics to the globe may in general strengthen under global warming. We fully agree\nthat such an amplification is plausible, given processes such as stronger wind responses arising\nfrom enhanced precipitation sensitivity to tropical SST anomalies and increased mean humidity\ndifferences in a warmer climate. These processes are not unique to ENSO and may indeed\napply, to some extent, to other tropical variability modes.\nclimate models consistently indicate that ENSO remains the most dominant, spatially extensive,\nand recurrent driver of interannual tropical–global SST variability. Second, while other modes\nsuch as Indian Ocean variability or Atlantic variability also produce teleconnections, their\nsignals are generally smaller in amplitude, more regionally and seasonally confined, and less\ncoherent across models and observations. As a result, their global‐scale contributions, although\nimportant, are secondary compared to ENSO.\nTherefore, while we acknowledge the broader issue raised by the reviewer as an important\navenue for future research, we believe it is beyond the scope of the present study. Our objective\nhere is to isolate and clarify the ENSO pathway as the leading driver of tropical–global SST\nteleconnections. We have revised the manuscript to make this scope explicit and to highlight\nthat our focus on ENSO does not preclude the possibility of similar mechanisms operating in\nother tropical modes.\nRequests for additional analyses:\n4. In the analysis of maximum R2 value, it is not clear which values are related each other and\nwhich are independent, so it would help to understand the mechanism if a map of the lag \"tau\"\nat the maximum absolute value is drawn together. In MME, is there uncertainty in the spatial\ndistribution of the lag \"tau\"?\nAns) We quantify ensemble phase agreement for τ using the mode across CMIP6 34 models at\neach grid point (Fig. R2-2). We then count how many models fall within plus-or-minus one and\nplus-or-minus two phases of that center. The ocean fraction where at least half of the models\nagree is ~30% for the ±1 month criterion and ~60% for the ±2-month criterion, indicating\nsubstantial model dependence—regional coherence in some basins, but considerable spread\nelsewhere. Figure R2-3 further shows τ for CESM1 and the observations; the scatter marks\nregions where CESM1 lies within the ±2-month criterion relative to observations. Agreement\nis strong in some areas and weaker in others. Nevertheless, even without exact uniformity in τ,\nbroad phase coherence is evident, and the strengthening of the ENSO–SST teleconnection\n29\nremains robust across models (Extended Data Figs. 2 and 3). This robustness reflects that our\nresults are insensitive to the precise lag choice—the amplification persists across reasonable τ\nwindows. (See also our response to Specific Comment L81–L82)\nFig. R2-2 | τ in each CMIP6 models. Maps of τ in each CMIP6 models.\nrespectively. Stippling in b-d indicates corresponding to \"τ ± 2\" of CESM1. The white box\nshows the Equatorial Pacific region, to exclude El Niño SSTA itself.\n31\n5. In the analyses, MAM(0) is used as the baseline and the difference from it is analyzed to\njudge the maximum R2 value and conduct the mixed layer heat budget analysis. But the spatial\ndistribution of the maximum R2 value or the heat budget terms must depend not only on the\nvalues at the time lag \"tau\" but also on the pattern at MAM(0). How much of impacts do this\nbaseline values have?\nAns) To assess baseline sensitivity, we repeated the analysis with anchors shifted by ±2 months\naround MAM(0)—JFM(0), FMA(0), AMJ(0), and MJJ(0)—and recomputed the PD to 2100\nchange in R², with τ evaluated relative to each anchor. As shown in Figure R2-4, the spatial\npatterns are essentially unchanged (pattern correlation > 0.9), indicating that our results are\ninsensitive to the baseline choice and that baseline effects are negligible.\nFig. R2-4 | Changes in ENSO impacts on global SST based on different baseline. a JFM(0)\nbased, b FMA(0) based, c AMJ(0) based, and d MJJ(0) based calculated changes in R2 from\nthe PD to 2100 with different 𝜏 in CESM1. Stippling indicates regions that are statistically\nsignificant at the 95% confidence level using the bootstrap test.\n32\n6. In the Eq.1, not only the SFC term but also the other budget terms (the advection terms,\ndiffusion term) should be shown.\nAns) We are sorry for the ambiguity. We did not diagnose the model’s full mixed layer\ntemperature tendency ∂T /∂t nor close the complete budget. Instead, we used the mixed layer\nm\nheat budget as a diagnostic framework and quantified only the surface flux-driven component\nof the ENSO-related SSTA by time-integrating the surface flux term. The ocean dynamical\nterm is difficult to be calculated due to the availability of the necessary oceanic variables.\nTherefore, the residual (SST – SST ) collects all non-surface flux processes such as\nTOT SFC+MLD\nhorizontal/vertical advection, entrainment process, and diffusion. We have revised the Methods\nto explicitly state this and replaced phrases suggesting “SST tendency” with wording “surface\nflux contribution to the ENSO-driven SSTA”. We have also added clear sentences.\n(L306-313): We calculated the surface flux-induced SSTA in diagnostic framework, based on\nthe following mixed layer heat budget equation:\n𝜕𝑇 𝑄\n𝑚 𝑛𝑒𝑡\n= +𝑅𝑒𝑠, (1)\n𝜕𝑡 𝜌 𝐶 ℎ\n0 𝑝\nwhere T is the mixed layer temperature, Q is net surface flux (positive into the ocean), 𝜌\nm net 0\n(1027 kg m-3) is the reference density of seawater, C (3985 J kg-1 K-1) is the specific heat of\np\nseawater at constant pressure. We only used surface flux term and calculate the surface flux\ncontribution to ENSO-driven SSTA. The Res mainly represents contributions from ocean\ndynamical processes such as horizontal/vertical advection, entrainment, and diffusion.\n7. To understand the differences among MME models in the maximum R2 values and wind\nspeed responses, please add figures of the maximum R2 values and wind speed regressions for\neach model to the Extended Data Figures.\nAns) We have added the Extended Data Figure with the change in maximum R2 values and\nwind speed responses, respectively.\ndifference in maximum R2 between SSTA and DJF Niño3.4 in each CMIP6 models.\ndifference in absolute maximum of surface wind speed regressed onto DJF Niño3.4 from\nMAM(0) to JJA(1) between PD to 2100.\nSpecific comments:\nL32-L42: Shifts in the position of the westerly jets due to global warming should also be\nimportant in changing the teleconnection pattern. Please add this in the text as it is an\nimportant factor in understanding the process of this study.\nAns) We have added a statement on the role of westerly jet.\n(L41-43): Moreover, the changes in position and strength of westerly jet under greenhouse\nwarming are expected to modulate these teleconnections by altering the midlatitude\nwaveguide21,22.\nL81-L82: Is the time lag “tau” also similar at each grid point between CESM1 LE and the\nobservations?\nAns) Figure R2-3 depicts τ (peak‐lag) maps for CESM1 and for each observational dataset.\nTheir spatial patterns are similar, but clear differences are also found in some regions. The\ndifferences might arise from the systematic biases in ENSO teleconnection of the climate\nmodels. In order to check whether the systematic biases in τ are critical for the main arguments\nof the present study, we calculated R2 for each season and their changes under global warming.\nand JJA(1) in CESM1 and CMIP6 multi-model mean, respectively. Across all five seasons\nboth the CESM1 and CMIP6 multi-model mean indicate a consistent increase in R²,\ndemonstrating that our conclusions are not sensitive to the τ. Uncertainty is summarized by\nstippling in Fig. R2-8, where more than 70% of models agree on the sign of the R² change.\nTogether, these results show that the strengthening is robust in all lead-lag time rather than tied\nto a particular τ.\nFig. R2-7 | The changes in R2 in each lead-lag season in CESM1. a-e CESM1 difference in\nR2 between 1pctCO2 2085-2115 and present-day, evaluated at fixed seasons: a JJA(0), b\nSON(0), c D(0)JF(1), d MAM(1) and e JJA(1). Stippling indicates statistically significant at\nthe 95% confidence level using the bootstrap test.\nmean difference in R2 between 1pctCO2 2085-2115 and historical, evaluated at fixed seasons:\na JJA(0), b SON(0), c D(0)JF(1), d MAM(1) and e JJA(1). Stippling indicates more than 70%\nof models agree on the sign of R2 change.\nL90-L91: What are the reasons for the weakening of the ENSO impact in the tropical Indian\nOcean?\nAns) To address the reviewer’s comments, Figure R2-7 examines the equatorial Indian Ocean\nand compares four diagnostics—SST , SST , SST , and SST —together with time\nTOT SFC+MLD SFC MLD\nseries of SSTA regressed onto the DJF Niño3.4 SSTA. The gap between SST and SST\nTOT SFC+MLD\nindicates a substantial contribution from ocean dynamics in this region. Consistent with\nprevious studies, El Niño is linked to Indian Ocean variability through two phases: during the\n37\ngrowth phase, in addition to atmospheric heat-flux effects, dynamical processes (thermocline\nshoaling/upwelling and associated circulation changes) strengthen a Bjerknes feedback\n(Bjerknes, 1969), fostering the development of the Indian Ocean Dipole (IOD) and enhancing\ncooling in the eastern equatorial basin (Saji et al., 1999); during the decay phase, surface heat-\nflux anomalies favor the Indian Ocean Basin Mode (IOBM) and yield basin-wide warming\n(Klein et al., 1999; Cai et al., 2019).\nBecause our primary decomposition isolates the surface-flux pathway, the growth-phase\ndynamical cooling is not fully captured, implying that our thermodynamic estimate alone\ncannot fully explain SST evolution. This interpretation is supported by the diagnostics: SST\nTOT\nshows stronger cooling than SST , consistent with an enhanced dynamical contribution,\nSFC+MLD\nwhereas SST indicates that the surface-flux pathway itself strengthens under warming.\nSFC\nTaken together, these results suggest that the overall El Niño influence on the equatorial Indian\nOcean intensifies, but the decay-phase warm SST anomaly becomes weaker in 2100. This\ncompensation explains why the signal over the equatorial Indian Ocean appears muted (and\nlocally cooler) despite the increased ENSO impact.\nBjerknes, J. ATMOSPHERIC TELECONNECTIONS FROM THE EQUATORIAL PACIFIC.\nMonthly Weather Review 97, 163–172 (1969).\nCai, W. et al. Pantropical climate interactions. Science 363, eaav4236 (2019).\nSaji, N. H., Goswami, B. N., Vinayachandran, P. N. & Yamagata, T. A dipole mode in the\ntropical Indian Ocean. Nature 401, 360–363 (1999).\nKlein, S. A., Soden, B. J. & Lau, N.-C. Remote Sea Surface Temperature Variations during\nENSO: Evidence for a Tropical Atmospheric Bridge. (1999).\nvariability. a Same as Extended Fig. 5a for Equatorial Indian Ocean (60°E–85°E, 10°S–5°N).\nb Temporal evolution of SSTA regressed onto DJF Niño3.4 SSTA from MAM(0) to JJA(1),\nduring PD (blue line) and the 2100 period (red line) for Equatorial Indian Ocean. c Temporal\nevolution of SSTA changed by the only El Niño-induced surface flux (conducted with SFC\nregressed on DJF Niño3.4 SSTA). Red dots indicate statistically significant at the 95%\nconfidence level using the bootstrap test.\nPlease specify the number of ensembles in the CMIP6 1pctCO2 experiments for each model.\nAns) Revised.\n(L274-275): In addition, 34 models in the CMIP6 pre-industrial and 1pctCO2 scenarios are\nemployed, using one ensemble member per model for each experiment.\nAt this point, there is no explanation of the difference between \"SFC+MLD\" subscripts, \"SFC\"-\nonly subscripts, and \"MLD\"-only subscripts, which is confusing, so please revise the order of\nthe explanations.\nAns) Revised.\n(L113-126): We adapted the mixed-layer heat budget equation to decompose the contributions\nto the enhanced El Niño impacts. Previous studies have identified surface flux (SFC; positive\ninto the ocean) as a primary driver of SST responses to ENSO25,36,37. To quantify the\ncontribution of the SFC, we first define SST as the time-integrated the surface flux-\nSFC+MLD\nrelated term of the equation, which is determined by (i) El Niño-induced surface flux anomaly\nand (ii) climatological mixed layer depth (MLD, see Methods for details) Figure 2a shows that\n. \nthe magnitude of the SST is larger than SST in both PD and 2100, possibly due to\nSFC+MLD TOT\nthe damping role of the ocean dynamics. These results suggest that the SFC is a primary driver\ninducing ENSO-driven global SSTA at least on a global average. Additionally, the changes in\nSST between PD and 2100 are comparable to the change in SST , indicating that the\nSFC+MLD TOT\namplified El Niño impacts can be attributed to anomalies induced by SFC.\nTo separate the mechanism, we decompose SST into (i) SST , which holds MLD fixed\nSFC+MLD SFC\nat its PD climatology, and (ii) SST , which holds the surface flux anomaly (SFC anomaly\nMLD\nregressed onto DJF Niño3.4) fixed at its PD value (see Methods for details).\n13) L284: Why did you use MAM(0) as the baseline? Why were DJF(0) and JFA(0) skipped?\nAns) We anchor at MAM(0) because it marks the onset of El Niño growth, whereas D(-1)JF(0)\n(mature), JFM(0) and FMA(0) (transition) emphasize non-onset phases. An onset baseline\nreduces phase-mixing between peak and decay and focuses the timing metric on teleconnection\ndevelopment. We estimate τ within ±2 months of the anchor and compare PD vs. 2100. Figure\nR2-3 shows that sensitivity tests with adjacent anchors yield near-identical spatial patterns:\npattern correlations with the MAM(0) fields are 0.93 (JFM(0)), 0.97 (FMA(0)), 0.97 (AMJ(0)),\nand 0.94 (MJJ(0)). Thus, our conclusions are robust to the baseline; D(-1)JF(0), JFM(0), and\nFMA(0) were not used to avoid phase-mixing outside the onset window.\nL486 (Fig. 3 caption): \"d-e\" -> \"e-f\"\nL487 (Fig. 3 caption): \"f\" -> \"g\"\nAns) Revised.\nThe manuscript investigates the changes in ENSO-related SST variability under global\nwarming based on climate models. It is a very interesting work suggesting that per-unit ENSO\nSST anomalies could induce amplified global-scale SST feedback responses under global\nwarming scenarios. The results carry significant implications for improving climate\nprojections of future variability. Through heat budget analysis using model data, the\nmanuscript indicates that intensified ENSO-related wind speed and air-sea humidity difference\nare the main factors contributing to the enhanced SST anomaly responses. To address distinct\nclimate regimes for different regions, the manuscript conducts diagnostics on four selected\nregions exhibiting specific atmospheric feedback responses.\nThe study is well designed and the manuscript is clearly written, guiding the reader towards\nits main conclusions. The methods section and the Extended file cover necessary details on\ndatasets, analysis and experiments.\nWe appreciate the reviewer’s encouraging and constructive comments. The reviewer’s\ncomments were fully reflected in the revised manuscript, and we carefully checked and revised\nthe manuscript. Our responses to the specific comments are as follows:\nComments:\n1. I guess my main comment/question lies in why could the same unit of ENSO SST anomalies\nlead to a stronger wind feedback through intensified atmospheric convection in the 2100\nscenarios, compared with the PD simulation as showed in Figure.3. In other words, why could\nthe same magnitude of ENSO SSTA induce stronger atmospheric convection feedback in a\nwarming world? Is the cause attributed to changes in the atmospheric mean states affecting\nconvective processes, an acceleration in atmosphere feedback establishment, or other factors?\nI guess a deeper analysis or discussion would greatly improve the comprehension of the\nunderlying physical mechanisms in this manuscript.\nAns) Thanks for the insightful comment. The intensified precipitation responses to ENSO-\nrelated equatorial Pacific SST anomalies under global warming have been demonstrated in\nprevious studies, which provide plausible physical mechanisms. First, atmospheric warming\n42\nincreases column water vapor approximately at the Clausius–Clapeyron rate, so that a given\nSST anomaly induces stronger precipitation and diabatic heating (Yun et al., 2020; Huang et\nal., 2015). Second, El Niño–like mean state changes promote an eastward extension of ENSO-\nrelated convection anomalies (Power et al., 2013; Cai et al., 2014; Kug et al. 2010; Geng et al.\n2023). As a result, the same SST anomaly induces a larger atmospheric response—manifested\nas stronger convective heating and circulation changes under warming (Hu et al., 2021). To\nexplicitly reflect these mechanisms, we revised the results section as follows:\n(L165-176): The intensified El Niño–related wind responses are likely linked to enhanced\nconvective responses driven by ENSO SST forcing. As shown in Fig. 3d, positive precipitation\nanomalies over the central and eastern Pacific become stronger and extend farther eastward\nunder greenhouse warming. Such intensified precipitation responses have been documented in\nprevious studies, which proposed plausible physical mechanisms42. First, atmospheric warming\nincreases column water vapor at approximately the Clausius–Clapeyron rate, so that a given\nSST anomaly produces stronger precipitation and diabatic heating16,43. Second, El Niño–like\nmean state changes favor an eastward extension of ENSO-related convection\nanomalies13,14,18,44. Consequently, the same El Nino-related SST anomaly induces a larger\natmospheric response—manifested as stronger convective heating and circulation changes\nunder warming33. This intensified atmospheric circulation, in turn, leads to stronger anomalous\nsurface wind speeds, thereby modulating evaporation through enhanced air–sea interaction.\nYun, K.-S. et al. Increasing ENSO–rainfall variability due to changes in future tropical\ntemperature–rainfall relationship. Commun Earth Environ 2, 1–7 (2021).\nHuang, P. & Xie, S.-P. Mechanisms of change in ENSO-induced tropical Pacific rainfall\nvariability in a warming climate. Nature Geosci 8, 922–926 (2015).\nPower, S., Delage, F., Chung, C., Kociuba, G. & Keay, K. Robust twenty-first-century\nprojections of El Niño and related precipitation variability. Nature 502, 541–545 (2013).\nCai, W. et al. Increasing frequency of extreme El Niño events due to greenhouse warming.\nNature Clim Change 4, 111–116 (2014).\nover North Pacific–America in the global warming simulations. Theor Appl Climatol 100, 275–\n282 (2010).\nGeng, X., Zhao, J. & Kug, J.-S. ENSO-driven abrupt phase shift in North Atlantic oscillation\nin early January. npj Clim Atmos Sci 6, 80 (2023).\nHu, K., Huang, G., Huang, P., Kosaka, Y. & Xie, S.-P. Intensification of El Niño-induced\natmospheric anomalies under greenhouse warming. Nat. Geosci. 14, 377–382 (2021).\nSpecific comments:\nI suggest the manuscript could include some analyses on the changes in ENSO amplitude\n(PD .vs. 2100) from CESM1 runs. On this basis, the manuscript further examines the global\nSST variations induced by per unit amplitude of ENSO SSTA—even though this does not\nfundamentally alter the work's main conclusions.\nAns) Thanks for the helpful suggestion. We agree that assessing the total ENSO influence on\nSST—rather than only the per-unit amplitude response—is important. We quantify ENSO’s\noverall SST impact as the product of (i) the sensitivity of SST to a unit-strength ENSO event\n(the regression coefficient, β) and (ii) ENSO amplitude (the standard deviation of the ENSO\nindex, σ). Following the regression-based decompositions of Lin et al. (2024) and Wu et al.\n(2021), we separate the projected change in this total impact into (a) a non-amplitude term\nlinked to changes in β, (b) an amplitude term linked to changes in σ, and a small second-order\nresidual. The formal definitions and implementation details are provided in the Extended Data\nText.\nDecomposition of changes in El Niño impacts on SSTA\nThe changes in El Niño impacts on SST is calculated as follows:\n𝑌′ = 𝛽×Niño3.4+𝑅𝑒𝑠 , (𝑆1)\n1\nwhere Y' is SSTA(𝜏), β is the regression coefficient, which indicates ENSO-induced SSTA,\nand Res is the residual. Unit of β is K per 1K of DJF Niño3.4 SSTA (DJF Niño3.4). We used\n1\nthe | β | as a representation of ENSO impacts on SSTA.\nconducted with DJF Niño3.4 , which represent standardized Niño3.4 SSTA as follows:\nstd\nNiño3.4 = Niño3.4/𝜎\n𝑠𝑡𝑑\n𝑌′ = 𝛽 ×Niño3.4 +𝑅𝑒𝑠 , (𝑆2)\n𝑠𝑡𝑑 𝑠𝑡𝑑 2\nwhere σ is the standard deviation of DJF Niño3.4, β is the regression coefficient indicating\nstd\nENSO-induced SSTA per one-standard deviation of ENSO, and Res is the residual. β\n2 std\nconsists of two influences: (i) non-amplitude factor (the regression coefficient, β) and (ii)\nENSO amplitude factor (the standard deviation of the ENSO index, σ). Since the |β |≈ |β| × σ\nstd\nfrom Eq. S2 and S3, decomposition of these two factors is calculated as follows:\n∆|𝛽 | = ∆|𝛽|×𝜎 +|𝛽 |×∆𝜎+𝑅𝑒𝑠 , (𝑆3)\n𝑠𝑡𝑑 𝑃𝐷 𝑃𝐷 3\nwhere ∆ means difference between 2100 and PD periods, the subscript PD indicates the PD\nperiod, Res is the residual term. Using Eqs. S2 and S3, we decompose the contribution of\n3\nnon-amplitude and amplitude factor.\nApplying this framework, we found that the increase in ENSO’s SST influence is dominated\nby the non-amplitude component. In CESM1, the non-amplitude pathway explains ~64% of\nthe total increase, while amplitude changes account for ~32% (with a small residual). In the\nCMIP6 multi-model mean, the dominance of the non-amplitude term is even clearer (~76%),\nwith amplitude contributing ~20% and a minor residual. These results indicate that\nbackground-state and teleconnection-related changes in SST sensitivity to a unit ENSO\namplitude play the leading role in amplifying ENSO’s overall SST impact, consistent with our\nmain finding that strengthened El Niño-induced wind-speed anomalies and enhanced\nclimatological air-sea humidity contrast drive the projected amplification. The relatively\nmodest amplitude contribution in the CMIP6 mean also aligns with the interpretation of\nExtended Data Figure 15.\nConsistent with this interpretation, Figures R3-1 a and e show that the per-unit-amplitude\nimpact on SST strengthens in both CESM1 and CMIP6. To make the total-impact perspective\nexplicit, we have added Extended Data Figure 14, which maps the total change and its\ndecomposition (non-amplitude vs. amplitude), and we discuss these contributions in the revised\nDiscussion. We also reference Extended Data Figure 15 when summarizing the amplitude\ncomponent and its comparatively modest role in the CMIP6 mean.\nthe projected increase in ENSO’s SST influence is the enhanced sensitivity—consistent with\nour main result that strengthened El Niño-related wind-speed anomalies and an enlarged\nclimatological air–sea humidity contrast intensify the thermodynamic pathway under warming.\n(L237-245): To quantify ENSO’s total SST influence, we decomposed it into a non-amplitude\nterm and an amplitude term, following regression-based approaches (Extended Data Text). The\nresults show that the increase is dominated by non-amplitude component changes—~64% in\nCESM1 and ~76% in the CMIP6 multi-model mean—while amplitude accounts for ~32% and\n~20%, respectively; importantly, even models with reduced ENSO amplitude54–56 show a net\nincrease because atmospheric and oceanic sensitivity strengthens (Extended Data Fig. 15 and\n16). In summary, background-state/teleconnection changes dominate, but amplitude changes\nare not negligible.\nLin, S., Dong, B. & Yang, S. Enhanced Impacts of ENSO on the Southeast Asian Summer\nMonsoon Under Global Warming and Associated Mechanisms. Geophysical Research Letters\n51, e2023GL106437 (2024).\nWu, R., Chen, W., Wang, G. & Hu, K. Relative contribution of ENSO and East Asian winter\nmonsoon to the South China Sea SST anomalies during ENSO decaying years. Journal of\nGeophysical Research: Atmospheres 119, 5046–5064 (2014).\nabsolute values of SSTA(𝜏) regressed onto DJF Nino3.4 SSTA from the PD period to 2100.\nThe contribution of b amplitude factor and c non-amplitude factor. d Global mean of (1) the\nchanges in absolute values of SSTA(𝜏) regressed onto DJF Nino3.4 SSTA from the PD period\nto 2100, (2) non-amplitude factor, and (3) amplitude factor. e-h are as the same as a-d\n(respectively) but CMIP6 multi-model mean.\ntrigger an ENSO (D0JF1) event)? If present, would this phase-leading relationship necessitate\nlarger-amplitude SSTA forcing to produce equivalent ENSO SST anomaly intensities?\nAns) Figure R3-2 shows grid points where τ precedes the ENSO peak more than three months\n(JJA(0)–SON(0)); these occupy only 6.1% of the analyzed area. In addition, most months of\nthe 6.1% were SON(0), close to the ENSO mature season. Thus, while precursor SSTA (e.g.,\nan Atlantic Niño) can occur, its spatial footprint is small. Consequently, we do not view the\n2100 increase in ENSO-related SST variability as requiring larger-amplitude precursor SSTA\nto achieve comparable DJF ENSO anomalies. Any phase-leading influence is likely minor at\nthe basin scale.\nFig. R3-2 | Regions where τ leads the ENSO peak (JJA(0)-SON(0)) in CESM1. Map of\ngrid points where the period with the most significant deviation in correlation from the\nMAM(0) baseline (τ) is JJA(0), JAS(0), ASO(0), and SON(0).\nthat in PD. Does this finding suggest a strengthening of the linear response component in\nENSO's climatic impacts in 2100 due to more stable atmospheric stratification?\nAns) We appreciate the question. We constructed separate composites of global SST anomalies\nfor El Niño and La Niña events for the PD and for 2100, and we further examined the change\nin their absolute values (|2100|–|PD|; Fig. R3-3). Events are defined by DJF Niño-3.4 SSTA\nexceeding ±1 standard deviation (≥+1σ for El Niño; ≤−1σ for La Niña), and the temporal\nalignment follows the same protocol as the maximum R² analysis (see Methods for details).\nHere, however, we analyze composite amplitudes rather than correlations. Figure R3-3 shows\nwidespread increases even in a metric that includes nonlinear responses, indicating a stronger\ntotal ENSO impact, not merely greater linearity. Although the changes are asymmetric between\nEl Niño and La Niña, both phases exhibit coherent intensification, implying that the underlying\ndynamical pathways have strengthened. Accordingly, as we discussed before, we attribute the\nhigher R² primarily to stronger ENSO-driven deep convective heating (i.e., a stronger\nanomalous circulation) and an enhanced air–sea humidity contrast that increases latent heat\nflux efficacy, rather than more stable atmospheric stratification.\nPD, 2100, and the change in absolute magnitude (|2100|-|PD|), taking the absolute maximum\nover the JJA(0)–JJA(1). d-f As in a-c but for La Niña. Stippling denotes regions where the\nchange in absolute amplitude is statistically significant at the 95% confidence level using a\nbootstrap test. The white box marks the equatorial Pacific region to exclude ENSO SST itself.\nAns) Revised from “anomaly” to “anomalies”\nThere appears to be an inconsistency in Figure 3, where the annotations for panels (e) and (g)\nseem to be inadvertently swapped.\nAns) Figure 3 caption has been revised accordingly.\nIn Figure 4b-e, what does the hatches between the blue and red lines represent?\nAns) Revised the caption of Figure 4.\n(L537-540): Vertical line marks 𝜏 (See Methods for details). The hatches shows the net\ndifference between the 2100 and PD ENSO-induced latent heat flux curves (starting at the last\nmoment before τ when the two curves are equal and ending at τ). A hatched area indicates an\nenlarged net heat uptake/loss during that window.\nIn Figure 4b-e, unlike the SST results, the temporal evolution of regression coefficients for\nlatent heat flux appears to exhibit distinct peak timings (except SIO). Does this suggest an\nalteration in air-sea interaction processes between PD and 2100?\nAns) While the differing peak months of the latent heat flux regressions could indicate changes\nin air–sea coupling in principle, we interpret them as modest phase shifts of the same basic\ncoupling mechanism (set by wind speed and air-sea humidity contrast) in a warmer mean state.\nWarming shifts the seasonal background circulation and humidity gradient, enabling the latent\nheat flux maximum to occur about 1 month earlier or later. In contrast, SST integrates these\nfluxes; therefore, its peak remains the same with present-day despite the latent heat flux\nmaximum timing shift. Therefore, the interaction does not occur in a fundamentally different\nway, which does not affect our conclusions.\nJJA(1)) in Methods.\nAns) Thank you for pointing this out. The difference is intentional, and we have revised the\nMethods to state it explicitly. Specifically, τ is an SST-based quantity defined as the month with\nthe maximum explained variance within JJA(0)–JJA(1) (relative to MAM(0)), reflecting the\nusual post-spring peak of the teleconnection. By contrast, the forcing peak (wind speed\nregression) is identified as the month with the largest absolute regression magnitude over\nMAM(0)–JJA(1), allowing the forcing to lead τ. We revised the Methods as following for\nclarity, explicitly stating the two search windows and the reasoning behind them.\n(L327-330): For the maximum wind speed forcing, we identify the month with the largest\nabsolute regression onto DJF Niño3.4 SSTA within MAM(0)-JJA(1) (including MAM(0)-\nMJJ(0)). We allow this to differ from τ because surface flux forcing generally precedes the SST\nresponse, so its timing may lead τ.\nThe authors have satisfactorily addressed all my previous concerns, and I recommend the\nmanuscript for acceptance.\nResponse to Reviewers #2 & #3:\nReviewers #2\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nReviewers #3\nI sincerely appreciate the authors’ conscientious and thoughtful responses. While I believe that\nmany of the points have been addressed appropriately, I feel that the response regarding the\npart that I find most questionable—the understanding of the process—has not been explained\nsufficiently.\nComments:\nMy main concern is that, regardless of whether the forcing originates from ENSO, CP, or EP,\nthe maximum R2 pattern and the wind speed response appear quite similar. This suggests that\nthe phenomenon may no longer be specific to ENSO. By focusing exclusively on ENSO, I am\nconcerned that the authors may be overlooking a more fundamental and general process that\ndoes not depend on ENSO.\nAns) We agree with the reviewer that our results suggest the operation of a more general\ntropical-forcing mechanism. Our additional analyses for the Indian and Atlantic Oceans indeed\nsupport this broader interpretation. Nevertheless, in this study we intentionally focus on ENSO,\nbecause it is by far the strongest, most spatially extensive, and most robust source of interannual\ntropical SST forcing and global teleconnections in both observations and models. While the\nsame physical processes may apply to other tropical modes, their impacts are weaker and more\n1\nregionally confined. We therefore believe that framing the analysis around ENSO provides the\nclearest and most relevant context, while we explicitly acknowledge in the Discussion that the\nunderlying mechanism is likely applicable to tropical SST forcing more generally.\nIn Fig. R2-1, the authors state that the agreement between panels (a, b, c), and d is less than\n20%; however, to my eye, the pattern correlations appear to be reasonably high. I do not think\nperfect coincidence is necessary, so I recommend examining the comparisons more carefully.\nAns) Thanks for the thoughtful suggestion. We also agree with the reviewer. Following the\nreviewer’s recommendation, we calculated the difference in the timing of peak variance (in\nmonths) between ENSO-induced windspeed variability and climatological wind variability\n(Fig. R3-1). As noted by the reviewer, the peak-month difference is within ±1 month for more\nthan 50% of the grid points, indicating that the two largely share their maximum variance\nwithin the same season. Given the strong seasonality of internal atmospheric variability (e.g.,\nwinter in the extratropics), the ENSO-forced wind response can be amplified during these\nperiods. More generally, the magnitude of ENSO-forced signals depends not only on the\namplitude of tropical diabatic heating but also on the amplification of its teleconnection, which\nis closely linked to regional atmospheric instability.\nclimatological wind-variance maximum. a-c show global maps of the minimum cyclic\ndifference (0-6 months) between (i) the peak timing of wind-speed regressed on a Niño3.4,\nb Niño3 and c Niño4 and (ii) the timing of the climatological wind variance maximum at each\ngrid point. Difference are computed on a 12-month cycle so that adjacent months across the\nyear boundary are treated as close in time. The ENSO reference region (white box) are excluded.\nIn the response to my Comment 2, the authors state that the pattern correlation between the\n3\nclimatological mixed layer depth (MLD) and the peak R2 was sufficiently small. However, are\nyou not including the tropical Pacific in the correlation calculation? As the authors mentioned\nin another section, the auto-correlation in the tropical Pacific should be excluded from the\nanalysis. Please check whether a significant negative correlation appears outside the tropical\nPacific.\nAns) In our previous response, the pattern correlations between the climatological mixed layer\ndepth (MLD) and the peak R² were already computed after masking out the tropical Pacific,\nin line with our statement that the strong autocorrelation in the tropical Pacific should not be used\nfor this diagnostic. Specifically, in both the previous and current analyses, grid points within\n10°S–10°N and 150°E–80°W were excluded before calculating the pattern correlations.\nWith this masking applied, the pattern correlation between present-day (PD)\nclimatological MLD and the peak R² is r = –0.05, and the correlation between PD\nclimatological MLD and the change in peak R² is r = 0.02. Both values are very small and\nstatistically insignificant, indicating that regions with large peak R² or enhanced peak R² under\nwarming are not systematically co-located with shallow MLD outside the tropical Pacific.\nWould there not also be a meaningful correlation between the changes in MLD under global\nwarming and the corresponding changes in peak R2? I encourage the authors to examine this\nrelationship carefully.\nAns) To address the reviewer’s suggestion, we examined the relationship between changes in\nclimatological MLD under global warming and the corresponding changes in peak R². We\ncomputed the area-weighted pattern correlation between the change in climatological MLD\n(future minus present-day) and the change in peak R², again masking out the tropical Pacific\n(10°S–10°N, 150°E–80°W) to avoid the effect of strong local autocorrelation there. The\nresulting correlation is r = 0.03, which is small and not statistically significant. This confirms\nthat regions where peak R² increases under warming are not systematically collocated with\nregions of MLD shoaling or deepening, supporting our conclusion that MLD changes play at\nmost a secondary role in organizing the pattern of enhanced ENSO-induced SST variability.\nOcean basins show similar behavior. I believe it is important to carefully consider whether the\nlogical development of the paper should start specifically from ENSO, or rather from tropical\nforcing more broadly.\nAns) Thanks again for the constructive comments. As we pointed out in the previous response,\nwe fully agree with the reviewer that the underlying mechanism is not strictly unique to ENSO\nand may operate more generally for other tropical modes. To address this point more directly,\nwe have carried out new partial linear regression analyses using Indian Ocean and tropical\nAtlantic indices. In these calculations, we use the Dipole Mode Index to represent tropical\nIndian Ocean variability (DMI; Saji et al., 1999) and the Atlantic 3 Index (ATL3; 20°W–0°,\n2.5°S–2.5°N; Zebiak, 1993 and Hu et al., 2022) to represent tropical Atlantic variability, taking\nSON as the mature season for DMI and JJA as the mature season for ATL3. We form 3-month\nseasonal means of SST anomalies and of each index, and for each basin we construct a ±3-\nmonth lag window around its mature season (i.e. seven 3-month seasons; from JJA(0) to\nD(0)JF(1) for DMI and from MAM(0) to SON(0) for ATL3). We then compute partial linear\nregressions of the SST anomalies onto each basin index while statistically removing ENSO-\nrelated variability by including the Niño3.4 index as a control variable. For each grid point and\neach basin, we evaluate the partial correlation and corresponding regression coefficient at all\nseven lags, define the “peak” response as the lag at which the absolute partial correlation is\nmaximized, and compare the present and warming climates in terms of the change in this peak\npartial regression amplitude.\nThese new results show that, even after removing ENSO, both the Indian Ocean and\ntropical Atlantic modes exhibit an increase in their remote SST influence under warming: the\nSST responses increase from the present to the warming climate for each tropical mode (Fig.\nR2-2). These results support the reviewer’s view that the mechanism we highlight is not\nconfined to ENSO alone but can also operate for other tropical forcings (modes).\nAt the same time, ENSO remains the dominant contributor to global SST variability.\nThe SST responses per 1 standard deviation Niño3.4 are substantially larger than the\ncorresponding DMI or ATL3 responses in both climates, and the increase under warming is\nalso largest for Niño3.4. In the CMIP6 multi model mean, the change in SST responses is\n5\napproximately 0.097 K for Niño3.4, compared to 0.060 K for DMI and 0.055 K for ATL3.\nAccordingly, while the same mechanism can apply across tropical basins, the IOD and ATL3\neffects remain clearly secondary to ENSO in terms of the magnitude of their global\nteleconnection influence. Thus, as we outlined in our previous response, our primary goal in\nthis study is to clarify the ENSO pathway because ENSO remains the strongest, most spatially\nextensive, and most recurrent driver of interannual tropical–global SST variability, whereas\nIndian and Atlantic modes typically exhibit weaker, more regionally confined, and less model-\nrobust teleconnections. For this reason, we would like to keep focus on the ENSO impacts,\nbut we explicitly discussed this broader interpretation in the Discussion section by adding a new\nparagraph.\n(L251-262): Although our analysis focuses on ENSO, the mechanisms we identify are not\nexclusive to ENSO and are likely relevant to tropical SST forcing more generally. To test this,\nwe performed additional partial linear regression analyses using indices for the Indian Ocean\nand the tropical Atlantic, after statistically removing ENSO-related variability. The results\nshow that these modes also display a strengthened influence on remote SSTs under warming\n(Supplementary Fig. 17). This consistency supports our interpretation that the amplification of\nENSO impacts mainly results from (i) stronger diabatic heating and the associated atmospheric\nteleconnections, and (ii) a larger air–sea specific humidity contrast—processes that can\nlikewise operate for other tropical SST forcings. A more comprehensive assessment is, however,\nneeded to fully quantify the enhanced impacts of non-ENSO tropical modes, which we leave\nfor future work.\na Same as Supplementary Fig.2a but for response to 1 standard deviation of Niño3.4. b Indian\nOcean Dipole effect, defined as the difference between the future (2100) and present-day (PD)\nclimates in the global mean absolute partial regression coefficient of SST anomalies onto the\nstandardized Dipole mode index (DMI) at the lag of maximum partial correlation (from JJA(0)\n7\nto D(0)JF(1)), after statistically removing ENSO variability with the Niño3.4 as a control\nvariable. c Same as b but for the tropical Atlantic, using the ATL3 index (20°W–0°,\n2.5°S–2.5°N). For each 34 CMIP6 model, multi-model mean (MME) and CESM1.\nSaji, N. H., Goswami, B. N., Vinayachandran, P. N. & Yamagata, T. A dipole mode in the\ntropical Indian Ocean. Nature 401, 360–363 (1999).\nZebiak, S. E. Air-Sea Interaction in the Equatorial Atlantic Region. Journal of Climate 6, 1567-\n1586 (1993).\nHu, Z.-Z. et al., Global Ocean Monitoring and Prediction at NOAA Climate Prediction\nCenter: 15 Years of Operations. Bulletin of the American Meteorological Society 103, E2701-\nE2718 (2022).\nI appreciate the authors’ diligent effort in revising this manuscript and providing a point-by-\npoint response. My previous major comments have been satisfactorily addressed. However, I\nguess a minor point need to be addressed to finalize the paper.\nComments:\n1: I appreciate the significant effort and thoroughness in revising the manuscript. The addition\nof the quantitative decomposition analysis, separating the ENSO impact on global SST into\nnon-amplitude and amplitude components, is a substantial and excellent improvement that\ngreatly strengthens the rigor of your conclusions. However, the changes in ENSO amplitude\nremain unclear for the CESM1 and CMIP6 runs in this study. As the authors stated in the\nrevised manuscript, ENSO amplitude changes should not be negligible, even though the\nnon-amplitude component of ENSO plays a more significant role in influencing global SST.\nAns) We agree that ENSO amplitude changes are not negligible and can directly affect remote\nSST responses. To clarify this point, we revised the Discussion to explicitly state that ENSO\nteleconnections are proportional to the strength of tropical ENSO forcing, so an increase\n(decrease) in ENSO amplitude would be expected to amplify (dampen) the remote SST\nresponse, all else equal. We also clarified that ENSO amplitude changes are not robustly\nconsistent across CESM1 and CMIP6, with the sign and magnitude varying across models and\nmetrics, implying substantial uncertainty in the amplitude contribution.\n(L236-250) : Though our study focused on changes in the SST sensitivity to ENSO, the actual\nENSO impacts will also depend on changes in ENSO amplitude itself. Importantly, changes in\nENSO amplitude are not clearly consistent across CESM1 and CMIP6 models, with the sign\nand magnitude varying across models. Because ENSO teleconnections tend to increase with\nthe strength of tropical forcing, an increase (decrease) in ENSO amplitude would be expected\nto amplify (dampen) the remote SST response. To quantify ENSO’s total SST influence, we\ndecomposed it into a non-amplitude term and an amplitude term, following regression-based\napproaches (Supplementary Notes). The results show that the increase is dominated by the\nchanges in non-amplitude component—~64% in CESM1 and ~76% in the CMIP6 multi-model\n9\nmean—while amplitude accounts for ~32% and ~20%, respectively; importantly, even models\nwith reduced ENSO amplitude54–56 show a net increase because atmospheric and oceanic\nsensitivity strengthens (Supplementary Figs. 15 and 16). Thus, while amplitude changes are\nmodel-dependent and introduce uncertainty in the total response, the strengthening of\nbackground-state and teleconnection sensitivity provides a more robust and consistent\ncontribution across experiments.\nI appreciate the authors' careful consideration of my comments. The authors have now\nexplicitly discussed the influence of other ocean basins in the Discussion section, and I think\nthis largely addresses my concerns. I have only one minor comment.\nComments:\nIn Supplementary Fig. 17, the use of partial linear regression is not fair, because it removes all\nvariability in the Indian and Atlantic Oceans that covaries with ENSO. For a fair comparison,\neither (1) Supp. Fig. 17b and c should also use standard (non-partial) regression, or (2) the\ninfluences of the Indian and Atlantic Oceans should be removed from the ENSO analysis in\nSupp. Fig. 17a (and similarly, the influences of ENSO and the Atlantic Ocean should be\nremoved from the Indian Ocean analysis, and likewise for the Atlantic Ocean).\nIf this point is revised, I believe the manuscript would be suitable for acceptance.\nAns) Thank you for this helpful suggestion. We agree that applying partial linear regression to\nthe Indian Ocean and Atlantic indices can remove the component of their variability that\ncovaries with ENSO, which may hinder a fair comparison with the standard ENSO regressions.\nFollowing your option (1), we performed additional calculations using standard (non-partial)\nlinear regression for the Indian Ocean and tropical Atlantic modes and revised Supplementary\nFig. 17b and c accordingly (i.e., regression of SST anomalies onto DMI and ATL3 without\nremoving ENSO).\nFigure R1 summarizes the updated results. The standard-regression patterns and their\nwarming-induced strengthening are highly consistent with those shown in our previous\nanalysis, indicating that our interpretation is unchanged: the remote SST responses associated\nwith the Indian Ocean and tropical Atlantic modes also strengthen under warming, supporting\nour broader point that the underlying amplification mechanism can operate for tropical forcings\nbeyond ENSO. At the same time, the DMI and ATL3 teleconnection signals remain clearly\nweaker than those associated with Niño3.4, so our conclusion that ENSO provides the\ndominant global influence on interannual SST variability is unchanged.\nWe have replaced Supplementary Fig. 17b and c with these standard-regression results\n1\nand clarified the corresponding description in the Supplementary Information. We thank the\nreviewer again for this comment, which improved the methodological fairness and clarity of\nSupplementary Fig. 17.\nSame as Supplementary Fig.2a but for response to 1 standard deviation of Niño3.4. b Indian\nOcean Dipole effect, defined as the difference between the future (2100) and present-day (PD)\nclimates in the global mean absolute regression coefficient of SST anomalies onto the\nstandardized Dipole mode index (DMI) at the lag of maximum correlation (from JJA(0) to\nD(0)JF(1)). c Same as b but for the tropical Atlantic, using the ATL3 index (20°W–0°,\n2.5°S–2.5°N). For each 34 CMIP6 model, multi-model mean (MME) and CESM1.\n3"}]

Decision letter