AMOC Weakening Dominates Global Warming Impacts on Precipitation Over Brazil

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Vilela, P. Luca, S. Koseki, T. Silva, D. Veleda, N. Keenlyside This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6933933/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Nov, 2025 Read the published version in npj Climate and Atmospheric Science → Version 1 posted 10 You are reading this latest preprint version Abstract Global warming is expected to substantially weaken the Atlantic Meridional Overturning Circulation (AMOC). However, climate models disagree greatly on the magnitude of AMOC weakening due to the large uncertainties in climate change projections, especially in the tropics. Here, we show through multi-model analysis of (CMIP6) future climate change projections that AMOC weakening during the next century will strongly influence precipitation and its extremes over Brazil. Such weakening dominates over the direct global warming impacts, causing drying in the Amazon, while completely mitigating them in northeast Brazil. We trace this to a tropical Atlantic warming, consistent with weakened heat transport along the southern branch of the South Equatorial Current. This induces a cross-equatorial sea surface temperature gradient and changes in latent heat flux, shifting the intertropical convergence zone southward. Our findings highlight the need to reduce uncertainties in the AMOC response to global warming and its oceanic mediated influences on Brazilian climate. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Climate sciences/Climate change/Climate change impacts Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The Atlantic Meridional Overturning Circulation (AMOC) is a key part of the climate system, transporting ocean heat poleward [ 1 ] , influencing ocean heat sequestration [ 2 ],[ 3 ] , the Intertropical Convergence Zone (ITCZ) [ 4 ] and regional extreme weather events [ 5 ] . Some studies show that AMOC has weakened over the last century [ 6 ],[ 7 ],[ 8 ],[ 9 ] , while others find no discernible change [ 10 ],[ 11 ] . An abrupt decline of AMOC will have global consequences [ 12 ],[ 13 ] , with considerable changes in precipitation across the tropics [ 14 ] . A collapse of the AMOC could even counteract the drying impacts of global warming in regions of northern South America [ 15 ] . The influence of AMOC on tropical precipitation has been mostly discussed via compensating poleward atmospheric heat transport, with the position of the ITCZ reflecting a coupled ocean–atmosphere adjustment to interhemispheric disturbances in energy [ 16 ] . Moreover, the northward heat transport by AMOC establishes a persistent cross-equatorial thermal asymmetry, which directly influences low-level atmospheric boundary layer and conditions necessary for atmospheric convection [ 17 ] . Although both pathways influence ITCZ migration, it is the oceanic transport that initiates the imbalance through the Atlantic Ocean, underscoring the primary role of AMOC-driven ocean dynamics in shaping tropical precipitation distribution. The AMOC is a particularly important regulator of tropical Atlantic climate and may influence extremes over eastern Brazil. Consisting mainly of water from the Drake Passage and the Agulhas Current [ 18 ],[ 19 ] , the upper branch of the AMOC forms the southern part of the South Equatorial Current (SEC) [ 20 ] . This southeast-northwest flow transports heat to the southwestern Atlantic warm pool (SAWP)—a region with SST higher than 28.5°C and enhanced low-level water vapor convergence, both conducive to heavy precipitation in the Eastern Northeast Brazil (ENEB) region. Furthermore, latent heat flux variations over the southwest Atlantic [ 21 ] can increase atmospheric moisture content during the ENEB rainy season, which extends from austral autumn to the end of the winter [ 22 ] . In addition, when Easterly Waves interact with local circulations, they can cause increased moisture convergence and strong precipitation over the ENEB [ 23 ],[ 24 ],[ 25 ],[ 26 ], and this can result in flash floods and landslides [ 25 ],[ 27 ] . Here, we investigate the impact of future projected changes in AMOC on the tropical Atlantic SST, mean and extremes precipitation over Brazil, including the ENEB, the latter a vulnerable area to global warming. We use data from a multi-model ensemble (MME) of 29 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) [ 28 ] under the historical and Shared Socio-economic Pathway 5-8.5 (SSP5–8.5) simulations. We define future changes as the average of the period 2050–2100 relative to 1950–2000. The MME predicts a wide range of changes in AMOC, tropical Atlantic climate, and ENEB precipitation extremes. Taking advantage of these model uncertainties, we perform an intermodel analysis and identify a robust relation between weakening AMOC and precipitation (mean and extremes) over Brazil (see Methods). Results and Discussion AMOC changes in the Southern and Northern Hemisphere The MME projects a weakening of the AMOC at 20°S by 2050–2100 under SSP5-8.5 with an average decrease of 4 Sv, with some models indicating as much as a ~ 8 Sv decrease or 41% weakening (Supplementary Table S2). The AMOC weakens even more strongly in the North Atlantic, decreasing at 30°N by 7 Sv on average and up to 12 Sv in some models, the latter corresponding to a ~ 60% weakening (Table S2), corroborating other studies [ 29 ] . The weakening is strongest in models with stronger mean AMOC, however there is little relation between AMOC weakening and the magnitude of global warming (Tab. S2). This is consistent with earlier model intercomparison studies that have shown that AMOC will weaken with global warming and emphasised large model uncertainty [ 30 ],[ 31 ],[ 32 ],[ 33 ] . Linear regression analysis shows a tight relation between AMOC weakening in the South and North Atlantic, explaining 92% of the variance, with stronger weakening in the north (Supplementary Figure S1 ). To reduce the influence of different climate model sensitivity, we normalize the AMOC changes by dividing them by the global mean temperature of each model; nevertheless, the regression remains equally strong (R 2 = 0.92) without normalising (Fig. S1 .b). The greater AMOC weakening in the North Atlantic (~ 20% greater than the South Atlantic) suggests a buildup of mass and implies accumulation of ocean heat content in the tropical Atlantic. In the following analysis we use the AMOC index at 20°S, as it is closer to our region of interest, while being highly correlated with AMOC changes in the north. Response of Tropical Atlantic SST and ENEB Precipitation to AMOC weakening We now assess the response of tropical Atlantic SST and precipitation to a weakened AMOC through regression analysis of the CMIP6 MME. As for the ΔAMOC index, ΔSST and ΔPR are normalized by each model’s global warming (ΔGSAT) to minimize confounding influences (See the methods). The map of regression coefficients with ΔAMOC at 20°S as the predictor and ΔSST as a response variable, show statistically significant negative values (p-value < 0.05) over the equatorial and South Atlantic (Fig. 1 a). These negative values indicate that the projected slow-down of the AMOC at 20°S can induce an increase in SST. A warming of the tropical Atlantic is consistent with heat convergence from the weakening of the northward heat transport in the Atlantic, as implied by the stronger AMOC weakening at 30°N compared to 20°S (Fig. S1 ). The correlation between the ΔAMOC and ΔSST among the models in the tropical Atlantic is large and statistically significant (p < 0.05), with R 2 of 0.4 over all the SAWP (0° to -15°S and 34°W to 20°W). This indicates that around 40% of the uncertainties in projected warming of the tropical Atlantic (40–5°W and 20°S − 5°N) is related to weakening of the AMOC (Fig. 1 a). Similar regression analysis for precipitation, ΔPR, indicate that a weakening of AMOC can lead to a southward shift of the Atlantic ITCZ (Fig. 1 b), consistent with previous findings [ 14 ] . This can eventually lead to increased precipitation in the western equatorial Atlantic and eastern part of Brazil, and decreased precipitation in the north equatorial Atlantic, western Brazil and equatorial Africa. ΔAMOC explains up to 40% of the uncertainties in projected precipitation in southern ENEB, while it explains 20% or less of the projected uncertainty in the northern region (Fig. 1 b). For a better understanding, we examine these relations using indices. We calculate the SST change over the eastern tropical Atlantic area (ΔTA) (40°–5°W and 20°S − 5°N), where ΔAMOC explains most of the variance (Fig. 1 a). A strong and statistically significant (p-value < 0.05) negative correlation of almost − 0.7 (Fig. 1 c) indicates that the SST over the tropical Atlantic increases as AMOC at 20°S declines. The warming is consistent with accumulating heat content due to a weakened AMOC and northward heat transport in the Atlantic. In some models, the AMOC at 20°S weakens by up to 3 Sv per degree of global warming. According to the regression relation, this weakening contributes an additional ~ 0.2°C warming per degree of global warming in this region (Fig. 1 c). This is equivalent to a local amplification of global warming by ~ 30%. Previous investigations have established a substantial correlation between SST in the SAWP located in eastern tropical south Atlantic, and precipitation in the ENEB (ΔENEB) [ 22 ],[ 27 ],[ 25 ] . This is because as Atlantic SST increases in this region, sea level pressure drops, increasing atmospheric moisture, eventually promoting convection [ 34 ],[ 25 ] . The CMIP6 MME shows that projected changes in precipitation in the ENEB region are similarly related to future SST warming in the tropical Atlantic (Fig. 1 d). The ΔENEB has a statistically significant positive correlation to ΔSST of approximately 0.80 (p-value < 0.05). Although the MME mean indicates a decrease in precipitation in the ENEB by -0.07 mm/day per degree of global warming, Fig. 1 d indicates that whether a model predicts a precipitation increase or decrease in this region depends on the level of SST warming in the tropical Atlantic. Given the high correlation between ΔENEB and ΔTA (Fig. 1 d) and between ΔAMOC and ΔTA (Fig. 1 c), we further examine the relationship between precipitation and AMOC changes. The scatter plot between ΔENEB and ΔAMOC illustrates a negative linear relation, with increases in precipitation in ENEB correlated to decreases in AMOC (r ~ − 0.6; Fig. 1 e). The negative slope indicates that the AMOC slowdown compensates for decreasing precipitation in the ENEB, by accumulating ocean heat and warming the SAWP. As discussed below, this is associated with increased evaporation from the ocean, which could enhance moisture convergence and eddy moisture transport and thereby increase precipitation [ 22 ] . Latent Heat Flux changes on AMOC weakening We examine the linear relation between latent upward heat flux changes (ΔLHF) and ΔAMOC, from the MME, to better understand the influence on ENEB precipitation, which is related to moisture convergence over the western south tropical Atlantic. AMOC decline in the SSP585 scenario leads to an increase in the LHF (as the regression is negative) in the southern branch of the South Equatorial Current (sSEC), considered a zonal pathway of the AMOC upper branch and the northern limit of the South Atlantic Subtropical Gyre (Fig. 2 ). The statistically significant (p-value < 0.05) increase in LHF in the western south tropical Atlantic is consistent with a weakening-AMOC driven increase in SST in this region (Fig. 1 a). Likewise, in the central south Atlantic Subtropical Gyre, LHF decreases in response to the cooling associated with the weakening AMOC (Fig. 1 a). In response to these ocean driven SST changes the ITCZ migrates southward in the Atlantic, consistent with the AMOC-forced ITCZ southward shift in past warmer climates [ 35 ] . As the ITCZ shifts further south, LHF decreases in the equatorial region and to the north; in this region it appears that a decrease in LHF is associated with weaker winds and tends to warm the ocean [ 21 ] . Simultaneously, the positive ΔLHF to the south will also result in more water vapour convergence over the western south tropical Atlantic. Thus, the AMOC-decline driven increase in heat content of the upper ocean, increase heat fluxes and moisture content of the lower troposphere and shift the ITCZ south, all of which have a statistically significant (p-value < 0.05) impact on South American climate. In addition, when the wind is easterly, evaporation in the western south tropical Atlantic is expected to feed diabatic energy to easterly wave disturbances, which induce extreme precipitation in the ENEB [ 36 ],[ 25 ],[ 26 ] . The increase in precipitation over the land seems to drive the increase of LHF in the ENEB (Figs. 1 b and 2 ). Estimating the influence of AMOC decline on tropical precipitation and SST Motivated by the robust impacts of AMOC on precipitation and SST over tropical Atlantic and surrounding regions, we now quantify the contribution of AMOC decline to the total projected MME mean changes in tropical Atlantic precipitation and SST. These are compared to the more direct effects of global warming, which causes robust changes in the hydrological cycle [ 37 ] . We separate the changes conceptually into three parts: one related to the AMOC at 20°S, based on the regressions shown above (Fig. 1 a, b); the second is related to GSAT; and the third includes all changes unrelated to either AMOC or GSAT and is assumed to average to zero across the models. After normalising the variables by the ΔGSAT to reduce the impact of different rates of global warming in the different models, we estimate the spatial changes in precipitation and SST using regression analysis. This analysis framework is outline in the Methods section. The MME mean projects a uniform and positive ΔSST of up to 0.8°C per degree of global warming (Fig. 3 a). Of this, global warming (ΔGSAT) contributes directly to an increase of up to 0.7°C in the equatorial region and to the north (Fig. 3 c), while the AMOC weakening contributes to a warming around 0.1°C in the equatorial regional and to the south (Fig. 3 e). Note the AMOC at 20 °S is projected to decrease by 1.14 Sv/°C. We estimate changes by the end of the century under the SSP5-8.5 scenario, for which the MME mean indicates 3.5°C global warming and 4 Sv reduction in AMOC at 20 °S. For this level of warming and AMOC weakening our analysis indicates a direct global warming contribution of 2.5°C in the tropical Atlantic and an indirect contribution from AMOC weakening of 0.35°C. Regarding precipitation, the MME mean projects a decrease of 0.1 mm/day per degree of global warming in the north and northeast of Brazil and over the western equatorial Atlantic, while the decrease is much less in the ENEB (Fig. 3 b). The direct global warming contribution shows a greater decrease in precipitation that exceeds 0.2 mm/day/°C in the western equatorial Atlantic and 0.1 mm/day/°C and in the ENEB (Fig. 3 d). This is equivalent to a reduction of ~ 125 mm/yr per degree Celsius over the ENEB in a 3.5°C warmer world, as projected by the MME mean under the SSP5-8.5. These changes follow the expected dry-get-drier paradigm [ 37 ],[ 14 ] . Consistently, a larger decrease in northeast of Brazil precipitation is found in the high emission-low adaptation scenario (SSP5-8.5) compared to low emission-high adaptation scenarios [ 38 ] . The reduction in precipitation is particularly significant (p-value < 0.05) for the northeast region—the driest area in Brazil, which faces substantial climate risks; for example, the Pernambuco State with 89% of its area affected by drought and low precipitation is highly vulnerable to climate change [ 39 ],[ 40 ],[ 41 ] . AMOC-weakening compensates for the direct global warming impact on precipitation over the ENEB and the equatorial Atlantic, while over the north-west region (~ 10°N-10°S and ~ 70°W-65°W) it enhances the global warming impact (Fig. 3 f). Over the ENEB region, the MME mean AMOC-weakening of 1.1 Sv/°C increases precipitation by ~ 0.1 mm/day/°C almost exactly balancing the direct global warming contribution (Fig. 3 d and f). For ΔAMOC of -4 Sv in the MME mean under the SSP5-8.5 scenario, the precipitation is expected to increase by approximately 131 mm per year (Fig. 1 e). This value is ~ 11% of the contribution, compared with the annual average precipitation in the ENEB, which is approximately 1500 mm/y, according to the National Institute of Meteorology (INMET) ( https://portal.inmet.gov.br ). Over the Amazon region, the AMOC weakening and global warming reduce precipitation almost equally, with the AMOC signal predominant in the west (~ 60°W). These results are consistent with studies investigating AMOC shutdown experiments [ 15 ] . The AMOC weakening drives precipitation changes in the tropical Atlantic and in the ENEB through the SST changes. The AMOC-weakening warms the equatorial and south Atlantic, inducing a cross-equatorial SST gradient (Fig. 3 e). This causes the Atlantic ITCZ to shift south (Fig. 3 f), as its location is closely related to the cross-equatorial SST gradient [ 42 ],[ 43 ],[ 44 ] . This mechanism is found in model experiments with strong AMOC weakening [ 45 ],[ 46 ] . The increased precipitation in the ENEB is connected to the ITCZ southward shift (Fig. 3 f). Next, we examine AMOC-weakening impacts on extreme precipitation events in the ENEB, which is vulnerable to such extremes. Extreme precipitation changes In the ENEB, 60% of precipitation comes from mesoscale meteorological systems like Easterly Waves [ 47 ] . We identify changes of the extreme indices related to the AMOC at 20°S and global mean surface temperature (GSAT), and we normalize the variables by the ΔGSAT (see Methods). Here we investigate the impact of AMOC decline on extreme precipitation, considering the maximum amount of ΔPR accumulated over one day (ΔRx1day, mm/°C) and over five days (ΔRx5day, mm/°C), and daily precipitation > 99th percentile (ΔR99p, mm/°C). The extreme precipitation indices are similarly correlated to ΔAMOC (Fig. S3), although with varying degrees of intensity (Fig. 4 ). A weakening AMOC leads to substantial increase in the precipitation extremes (Fig. 4 d-f) over the Amazon, northeast, and southeast of Brazil as well as the center of SEC path and close to the Agulhas Current system. The changes in the ΔRx5day are more considerable over the Amazon. Therefore, we now estimate how much the AMOC reduction contribute to precipitation-extreme indices over Brazil and contrast them to the direct consequences of global warming, under the SSP5-8.5 scenario (Fig. 4 ). The MME mean projects a similar pattern of change for all three extremes precipitation indices (Fig. 4 a-c) per degree of global warming, but with different magnitude. There is an increase of ~ 3 mm/°C in ΔRx1day (Fig. 4 a) and ~ 5mm/°C in ΔRx5day (Fig. 4 b), and ~ 20 mm/°C in ΔR99p (Fig. 4 c) over the north part and northeast of Brazil, and north equatorial Atlantic. There is good agreement with more than 75% of the models having the same sign of change over the north and northeast of Brazil. Considering the direct global warming contribution, there is an increase in extremes precipitation over most of Brazil, except for a small region in the north (Fig. 4 d-f). The ΔRx1day (Fig. 4 d) and ΔRx5day (Fig. 4 e) show a decrease of over ~ -3 mm/°C in the north of Brazil. On the other hand, there are increases up 3 to 5 mm/°C over the southeast of the country (Fig. 4 d-e), and ~ 10 mm/°C for the ΔR99p (Fig. 4 f) over the northeast of Brazil with high agreement among the models. This is equivalent to an increase of ~ 18 mm in ΔRx5day (Fig. 4 e) over the northern part of the ENEB and southeast of Brazil in a 3.5°C warmer world, as projected by the MME mean under the SSP5-8.5 scenario. We observe an even stronger increase in the northwest of Africa with precipitation extremes expected to double. These changes are consistent with the wet-get-wetter paradigm under climate change [ 48 ] . AMOC-weakening drives a substantial increase in accumulated extreme precipitation in the north and northeast of Brazil, while the intensity increases in the north and decreases in the southwest part of Amazon. The increase of ~ 1 mm/ °C in ΔRx1day (Fig. 4 g), ~ 5 mm/ °C in ΔRx5day (Fig. 4 h) and ~ 10 mm/ °C in ΔR99p (Fig. 4 i) over north of Brazil compensates the decrease in extreme precipitation caused from the direct global warming part. For ΔAMOC of -4 Sv in the MME mean under the SSP5-8.5 scenario, the ΔRx5day increases up to 14 mm (Fig. 4 h) over the ENEB and 70 mm in north of Brazil. AMOC-weakening contributes to drier conditions (~ 5 mm/ °C) conditions over the southwest and wetter extreme conditions (~ 20 mm/ °C) in the north of the Amazon, 10 mm/ °C in the northeast, as well as in the ENEB where there is an increase ~ 5 mm/ °C for ΔR99p (Fig. 4 i). Like the mean precipitation changes, a weakened AMOC influence on extreme precipitation is of similar magnitude to the global warming impact and can even dominate the response over North Brazil. Conclusions We investigated how a possible future AMOC decline (defined as CMIP6 historical minus SSP5-8.5 simulations) modulates the tropical Atlantic SST, LHF, mean precipitation and precipitation extremes, by analyzing MME of CMIP6 models. As previous studies have shown, global warming is projected to bring warmer and drier conditions in north and northeast Brazil [ 38 ],[ 49 ] . The main effect of an AMOC weakening is to warm tropical Atlantic SST (Figs. 1 a, c and 3 e) and increase LHF, leading to wetter conditions in the ENEB and drier conditions in the north of Brazil (Fig. 1 b, e and 3 f). Thus, a weakened AMOC partially offsets the drier conditions expected from global warming in northeast Brazil and amplifies the drier and warmer scenario over northern Brazil, especially in the Amazon. We showed the role of the ocean in driving precipitation changes, in contrast to previous studies that focused on changes in the atmospheric poleward heat transport [ 50 ] . Our new findings on rainfall extremes are another novel aspect, which has revealed the intricate influences of AMOC decline on hydrometeorological climate in Brazil. Global warming is expected to increase extreme precipitation over most of Brazil. The AMOC weakening in some regions and for some extreme indices amplifies the global warming signal, while in others it offsets them: In the northwest of Brazil, the AMOC weakening intensifies extreme precipitation (R99p, Rx1day and Rx5day), while global warming reduces extremes in this region. In the ENEB, AMOC weakening intensifies the Rx5day and R99p, but compensates the Rx1day. Interestingly, in some regions the response of the precipitation extremes to the weakening AMOC are like that found for mean precipitation (e.g., ENEB for the Rx5day and R99p) while in other regions it is the opposite. The similar response of mean and extreme precipitation in the ENEB to the AMOC weakening is associated with the southward-shifted ITCZ and local ocean warming that leads to the wetter conditions, favoring the instability of meteorological systems [ 25 ] , and the intensification of the easterly waves [ 25 ],[ 26 ] . The opposite response of AMOC weakening on the mean and extreme precipitation in the northwest is like that of global warming, which can lead to increased temperatures and altered precipitation patterns. The impact of the AMOC weakening on the extreme and mean precipitation in the north of Brazil and on mean precipitation in the ENEB are robust, while the impact on extreme precipitation in the ENEB is less substantial. The robust impacts are associated with large-scale features, such as the southward ITCZ shift. The uncertainty for the ENEB could be linked to small-scale processes that are poorly resolved in climate models, such as easterly wave disturbances. This is also reflected in the inaccurate simulation of the seasonal rainfall cycle by the models, which typically show the maximum precipitation in February–March rather than the observed peak between May and July (Fig. S2). Despite these errors and uncertainties, an increase in precipitation extremes over the ENEB is expected due to regional SST warming in the western equatorial Atlantic, associated with AMOC weakening. These results suggest that the potential impact of global warming on severe precipitation events may be underestimated in the ENEB. This is a concern for the already vulnerable ENEB region where the extreme events are projected to increase. Further tools for investigation, such as CORDEX-South America ( https://cordex.org/domains/cordex-region-south-america-cordex/ ), could help understand model uncertainty. While global warming exerts drastic regional effects over Brazil, where the ENEB and the Amazon are at increasing risk of dryness, the AMOC change can substantially modulate these impacts. Our results show that the AMOC weakening can partially offset the projected drying in the ENEB while intensifying it in northern Brazil, especially in the Amazon region. Regarding the extreme precipitation, the AMOC acts as a compensating mechanism for climate change, adding complexity and uncertainty to future projections. In the ENEB region this modulation may result in more frequent events. These findings underscore the importance of reducing uncertainties related to the AMOC's response to global warming to better constrain regional climate projections and inform effective adaptation strategies in vulnerable areas. Methods CMIP6 Data We use MME of CMIP6 simulations [ 28 ] following the historical and SSP5-8.5 scenarios [ 51 ] , which is a high-end forcing scenario with a global mean radiative forcing of 8.5 W m − 2 by 2100 and low climate change adaptation strategies. We use the AMOC, SST, precipitation, global surface air temperature and surface upward latent heat flux data from 29 CMIP6 models (Table S1 in Supporting Information S1). The data is at monthly temporal resolution. The datasets, except for AMOC, are interpolated to a common 1° × 1° grid before analysis. Only one ensemble member for each model is used. For most models we selected member r1i1p1f1, but for 11 models, the r1i1p1f1 was not available when we accessed the database, hence we use other ensemble members (Table S1 in Supporting Information S1). For the analysis of precipitation extremes indices based on daily precipitation rates, we had access to data from 22 of the 29 models (Table S2 in Supporting Information S1). Framework for Assessing Changes in Climate Indices Our analysis focuses on the projected changes from 2050 to 2100 (SSP5-8.5) relative to 1950 to 2000 (for historical). We compute the change in a variable, V, for a particular model, m, as follows: Where the overbar represents an average over the different 50-year periods, and the Δ V represents the change of the variable. We analyze the following variables: the AMOC_20°S and AMOC_30°N indices, computed as the maximum of the ocean meridional overturning stream function at 20°S (the closest to 34°S found for most models) and at 30°N in the Atlantic for each year; The SST mean index (ΔTA) for the tropical Atlantic (40–5°W and 20°S − 5°N), as well as the precipitation mean index (ΔPR) for the ENEB region (36–34°W and 10–6°S) and the latent upward heat flux changes (ΔLHF). We examine the relationship between AMOC indices and each variable. We also analyze annual mean precipitation and Climpact indices ( https://climpact-sci.org/indices/ ) of annual total precipitation from very wet days (R99p), monthly maximum 1-day (Rx1day) and 5-day precipitation amount (Rx5day) [ 52 ] . R99p is defined as the annual sum of days when the precipitation exceeds the 99th percentile of wet-day precipitation amounts, with the percentile calculated over the baseline period 1981–2010 (Text S1 in Supporting Information S1). Isolating AMOC influences from other global warming impacts We use output from the MME to estimate the influences of AMOC (denoted ΔA m ) decline and other global warming (ΔG m ) influences on SST, mean and extremes precipitation assuming the following decomposition: Δ \(\:{\varvec{V}\varvec{V}}_{\varvec{m}}\left(\varvec{x},\varvec{y}\right)=\:\) Δ \(\:\:{\varvec{A}}_{\varvec{m}}\:{\varvec{P}}_{\varvec{A}}\left(\varvec{x},\varvec{y}\right)+{{{\Delta\:}\varvec{G}}_{\varvec{m}}\varvec{P}}_{\varvec{G}}\left(\varvec{x},\varvec{y}\right)+{\varvec{Z}}_{\varvec{m}}(\varvec{x},\varvec{y})\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(2\right)\:\) Where Δ VV represents the decomposition variable (PR, SST, Rx1day, Rx5day and R99p), subscript m denotes a specific model, and ΔZ m independent of ΔG m and ΔA m . In the following we neglect ΔZm, assuming that there are no other systematic influences on ΔPR, ΔSST, ΔRx1day and ΔRx5day and ΔR99p, while this assumption is questionable, the overall high explained variances of our results suggest it is reasonable. P A and G A are the spatial patterns associated with a unit change in AMOC and global warming. ΔG m is defined as the change in global mean surface air temperature. The CMIP models simulate a wide range of global warming, as they have diverse climate sensitivity. The large spread makes it difficult to isolate the direct impact of AMOC on SST, mean and extremes precipitation as they are strongly influenced by global warming. To reduce this confounding influence, we divide each term in Eq. (2) by ΔG m (*) [ 14 ] . Δ \(\:{{\varvec{V}\varvec{V}}_{\varvec{m}}\left(\varvec{x},\varvec{y}\right)}^{\varvec{*}}={\:{{\Delta\:}\varvec{A}}_{\varvec{m}}}^{\varvec{*}}{\varvec{P}}_{\varvec{A}}\left(\varvec{x},\varvec{y}\right)+{\varvec{P}}_{\varvec{G}}\left(\varvec{x},\varvec{y}\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(3\right)\:\) Here, we redefined separately the variables: $$\:{\Delta\:}{{\varvec{V}\varvec{V}}_{\varvec{m}}\left(\varvec{x},\varvec{y}\right)}^{\varvec{*}}=\frac{{\Delta\:}{\varvec{V}\varvec{V}}_{\varvec{m}}\left(\varvec{x},\varvec{y}\right)}{{\Delta\:}{\varvec{G}}_{\varvec{m}}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(4\right)\:$$ $$\:{{\Delta\:}\:\varvec{A}\varvec{m}}^{\varvec{*}}\:=\frac{{\Delta\:}{\varvec{A}}_{\varvec{m}}}{{\Delta\:}{\varvec{G}}_{\varvec{m}}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(5\right)\:$$ We estimate the spatial patterns using regression analysis across the ensemble of different models. Where \(\:m\) is the covariance computed across the models. $$\:{\varvec{P}}_{\varvec{A}}\left(\varvec{x},\varvec{y}\right)=\frac{{{}_{\varvec{m}}\:}{{{}_{\varvec{m}}\:}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(6\right)\:$$ From this equation, we estimate the change in SST, mean and extremes precipitation due to the mean AMOC change: $$\:{{\Delta\:}\:{\varvec{V}\varvec{V}}_{\varvec{A}}\left(\varvec{x},\varvec{y}\right)}^{\varvec{*}}=(\frac{1}{\varvec{M}}{\sum\:}_{\varvec{i}=1}^{\varvec{M}}\left({{\Delta\:}\:\:{\varvec{A}}_{\varvec{i}}}^{\varvec{*}}\right){)\varvec{P}}_{\varvec{A}}\left(\varvec{x},\varvec{y}\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:$$ 7 Where \(\:\frac{1}{\varvec{M}}{\sum\:}_{\varvec{i}=1}^{\varvec{M}}\left({{\Delta\:}\:\:{\varvec{A}}_{\varvec{i}}}^{\varvec{*}}\right)\:\:\:\) is the model mean change in AMOC (Sv) for 1-degree Celsius change in global mean temperature. Then we can estimate the change in SST, mean and extremes precipitation for 1-degree Celsius change in global mean temperature: $$\:{\varvec{P}}_{\varvec{G}}\left(\varvec{x},\varvec{y}\right)=\frac{1}{\varvec{M}}{\sum\:}_{\varvec{i}=1}^{\varvec{M}}\left({{\Delta\:}\:{\varvec{V}\varvec{V}}_{\varvec{i}}\left(\varvec{x},\varvec{y}\right)}^{\varvec{*}}\right)-\:{{\Delta\:}\:{\varvec{V}\varvec{V}}_{\varvec{A}}\left(\varvec{x},\varvec{y}\right)}^{\varvec{*}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(8\right)\:$$ The first term on the right is the multimodel mean. Statistical tests We compute the regression coefficients (slopes), Spearman rank correlation [ 53 ] and statistical metrics (R² and p-values) to understand consistency and divergence across simulations. In estimating statistical significance, we assume independence of each model. Therefore, we evaluated CMIP6 (Text S1 in Supporting Information S1) model agreement using ensemble spread analysis. Declarations Data availability The CMIP6 data used in this study is available from ESGF (https://esgf-index1.ceda.ac.uk/search/cmip6-ceda/) with references listed in Supporting Information S1 (Table S1). Code Availability Available on request to the corresponding author. Acknowledgments I. Vilela would like to thank the Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco (FACEPE) (Grant IBPG-1210-1.08/18) and CAPES-PRINT (Grant 88887.717529/2022-00). I. V. thanks to the Partnership for Observation of the Global Ocean (POGO) and the Scientific Committee on Oceanic Research (SCOR) for the fellowship awarded. All authors are grateful to the TRIATLAS Project, which has received funding from the European Union's Horizon 2020 research and innovation program under Grant Agreement Number 817578. I. V. and D. V. thank the SIMOPEC Project, funded by the Brazilian National Research Council (CNPq), Grant Number 406707/2022 and the Project Climate analysis and coupled ocean-atmosphere modeling of extreme events in response to warming in the Brazil-Malvinas Confluence Region - CNPq Grant 421049/2023-5. S. K. is supported by TRIATLAS project (EU 2020 Horizon, grant # 817578) and EUREC4A-OA project funded by the Research Council of Norway (Grant Number: 317267) under a joint JPI Climate and JPI Ocean call). N.K. and S. K. thank the Research Council of Norway SFI Climate Futures (Gant Number: 309562) Financial support This research has been supported by SIMOPEC Project, funded by the Brazilian National Research Council (CNPq), Grant Number 406707/2022. Author contributions I. 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Supplementary Files KVSKLVSupportingInformationNPJ.docx Cite Share Download PDF Status: Published Journal Publication published 17 Nov, 2025 Read the published version in npj Climate and Atmospheric Science → Version 1 posted Editorial decision: Revision requested 09 Jul, 2025 Reviews received at journal 09 Jul, 2025 Reviews received at journal 08 Jul, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers agreed at journal 23 Jun, 2025 Reviewers agreed at journal 20 Jun, 2025 Reviewers invited by journal 20 Jun, 2025 Editor assigned by journal 20 Jun, 2025 Submission checks completed at journal 20 Jun, 2025 First submitted to journal 19 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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The contours denote the explained variance (or R\u003csup\u003e2\u003c/sup\u003e). Grey dots indicate areas where the linear regression is statistically significant (p \u0026lt; 0.05). Scatter plots between c) ΔAMOC* at 20°S and ΔTA*; d) ΔTA* versus ΔENEB*(mm/day°C); and e) ΔAMOC* versus ΔENEB*. The orange lines represent the linear regressions. Asterisks (*) indicate that all the variables are divided by ΔGSAT (°C) for each respective CMIP6 model. SST in the tropical Atlantic (ΔTA) is represented by the yellow box in a), whereas PR in the ENEB region is represented in the magenta box.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6933933/v1/808c667da9306b2833210483.png"},{"id":85211740,"identity":"ec722d9d-f93b-43d9-9ebb-15904419ff0a","added_by":"auto","created_at":"2025-06-23 12:37:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":429428,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression map between ΔAMOC* at 20°S (Sv/°C), and surface upward latent heat flux changes (ΔLHF* – positive upward) (W/m2/°C), the contours denote the explained variance (or R\u003csup\u003e2\u003c/sup\u003e). Asterisks (*) indicate that the variables are divided by ΔGSAT for each respective CMIP6 model. Grey dotes indicate areas where the linear regression is statistically significant (p-value \u0026lt; 0.05).\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6933933/v1/344e1efcf4276ad943848641.png"},{"id":85211741,"identity":"42824f84-a632-4127-925b-261d09383e52","added_by":"auto","created_at":"2025-06-23 12:37:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1247787,"visible":true,"origin":"","legend":"\u003cp\u003eMME mean of a) ΔSST and b) ΔPR associated with 1 °C of ΔGSAT (°C); and contribution of ΔGSAT to c) ΔSST and d) ΔPR; contribution of ΔAMOC to e) ΔSST and f) ΔPR. The contours denote the explained variance (or R\u003csup\u003e2\u003c/sup\u003e). Asterisks (*) indicate that the variables are divided by ΔGSAT for each respective CMIP6 model. Stippling indicates grid cells where more than 75% of the models agree on the sign of change.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6933933/v1/daf906fe4ef117a77cbddc2a.png"},{"id":85211744,"identity":"e209f896-6556-410e-aa90-2701154d8f41","added_by":"auto","created_at":"2025-06-23 12:37:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":794305,"visible":true,"origin":"","legend":"\u003cp\u003eMME mean of a) ΔRx1day, b) ΔRx5day and c) ΔR99p associated with 1 °C of ΔGSAT (°C); contribution of ΔGSAT to d) ΔRx1day, e) ΔRx5day and f) ΔR99p; and contribution of ΔAMOC to g) ΔRx1day, h) ΔRx5day and ΔR99p. The contours denote the explained variance (or R\u003csup\u003e2\u003c/sup\u003e). Asterisks (*) indicate that the variables are divided by ΔGSAT for each respective CMIP6 model. Stippling indicates grid cells where more than 75% of the models agree on the sign of change.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6933933/v1/897887e1edec08703a02541f.png"},{"id":96650943,"identity":"51ac815f-9d8b-454b-8965-02d03df314a0","added_by":"auto","created_at":"2025-11-24 16:12:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3389507,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6933933/v1/6e2b2539-eee6-4c2d-a3e3-b24cc550d8cf.pdf"},{"id":85212815,"identity":"eef3a5ba-1491-495a-b553-fd08bee4fbf0","added_by":"auto","created_at":"2025-06-23 12:45:31","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":5082650,"visible":true,"origin":"","legend":"","description":"","filename":"KVSKLVSupportingInformationNPJ.docx","url":"https://assets-eu.researchsquare.com/files/rs-6933933/v1/4494bd6bf4e4df14b3037d67.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"AMOC Weakening Dominates Global Warming Impacts on Precipitation Over Brazil","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Atlantic Meridional Overturning Circulation (AMOC) is a key part of the climate system, transporting ocean heat poleward \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, influencing ocean heat sequestration \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e],[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e, the Intertropical Convergence Zone (ITCZ) \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e and regional extreme weather events \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Some studies show that AMOC has weakened over the last century \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e],[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e],[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e],[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, while others find no discernible change \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e],[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. An abrupt decline of AMOC will have global consequences \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e],[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, with considerable changes in precipitation across the tropics \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. A collapse of the AMOC could even counteract the drying impacts of global warming in regions of northern South America \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe influence of AMOC on tropical precipitation has been mostly discussed via compensating poleward atmospheric heat transport, with the position of the ITCZ reflecting a coupled ocean\u0026ndash;atmosphere adjustment to interhemispheric disturbances in energy \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Moreover, the northward heat transport by AMOC establishes a persistent cross-equatorial thermal asymmetry, which directly influences low-level atmospheric boundary layer and conditions necessary for atmospheric convection \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Although both pathways influence ITCZ migration, it is the oceanic transport that initiates the imbalance through the Atlantic Ocean, underscoring the primary role of AMOC-driven ocean dynamics in shaping tropical precipitation distribution.\u003c/p\u003e \u003cp\u003eThe AMOC is a particularly important regulator of tropical Atlantic climate and may influence extremes over eastern Brazil. Consisting mainly of water from the Drake Passage and the Agulhas Current \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e],[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, the upper branch of the AMOC forms the southern part of the South Equatorial Current (SEC) \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. This southeast-northwest flow transports heat to the southwestern Atlantic warm pool (SAWP)\u0026mdash;a region with SST higher than 28.5\u0026deg;C and enhanced low-level water vapor convergence, both conducive to heavy precipitation in the Eastern Northeast Brazil (ENEB) region. Furthermore, latent heat flux variations over the southwest Atlantic \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e can increase atmospheric moisture content during the ENEB rainy season, which extends from austral autumn to the end of the winter \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. In addition, when Easterly Waves interact with local circulations, they can cause increased moisture convergence and strong precipitation over the ENEB \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e],[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e],[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e],[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e],\u003c/sup\u003e and this can result in flash floods and landslides \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e],[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHere, we investigate the impact of future projected changes in AMOC on the tropical Atlantic SST, mean and extremes precipitation over Brazil, including the ENEB, the latter a vulnerable area to global warming. We use data from a multi-model ensemble (MME) of 29 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e under the historical and Shared Socio-economic Pathway 5-8.5 (SSP5\u0026ndash;8.5) simulations. We define future changes as the average of the period 2050\u0026ndash;2100 relative to 1950\u0026ndash;2000. The MME predicts a wide range of changes in AMOC, tropical Atlantic climate, and ENEB precipitation extremes. Taking advantage of these model uncertainties, we perform an intermodel analysis and identify a robust relation between weakening AMOC and precipitation (mean and extremes) over Brazil (see Methods).\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAMOC changes in the Southern and Northern Hemisphere\u003c/h2\u003e \u003cp\u003eThe MME projects a weakening of the AMOC at 20\u0026deg;S by 2050\u0026ndash;2100 under SSP5-8.5 with an average decrease of 4 Sv, with some models indicating as much as a\u0026thinsp;~\u0026thinsp;8 Sv decrease or 41% weakening (Supplementary Table S2). The AMOC weakens even more strongly in the North Atlantic, decreasing at 30\u0026deg;N by 7 Sv on average and up to 12 Sv in some models, the latter corresponding to a\u0026thinsp;~\u0026thinsp;60% weakening (Table S2), corroborating other studies \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. The weakening is strongest in models with stronger mean AMOC, however there is little relation between AMOC weakening and the magnitude of global warming (Tab. S2). This is consistent with earlier model intercomparison studies that have shown that AMOC will weaken with global warming and emphasised large model uncertainty \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e],[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e],[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e],[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLinear regression analysis shows a tight relation between AMOC weakening in the South and North Atlantic, explaining 92% of the variance, with stronger weakening in the north (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). To reduce the influence of different climate model sensitivity, we normalize the AMOC changes by dividing them by the global mean temperature of each model; nevertheless, the regression remains equally strong (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.92) without normalising (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.b). The greater AMOC weakening in the North Atlantic (~\u0026thinsp;20% greater than the South Atlantic) suggests a buildup of mass and implies accumulation of ocean heat content in the tropical Atlantic. In the following analysis we use the AMOC index at 20\u0026deg;S, as it is closer to our region of interest, while being highly correlated with AMOC changes in the north.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResponse of Tropical Atlantic SST and ENEB Precipitation to AMOC weakening\u003c/h3\u003e\n\u003cp\u003eWe now assess the response of tropical Atlantic SST and precipitation to a weakened AMOC through regression analysis of the CMIP6 MME. As for the ΔAMOC index, ΔSST and ΔPR are normalized by each model\u0026rsquo;s global warming (ΔGSAT) to minimize confounding influences (See the methods).\u003c/p\u003e \u003cp\u003eThe map of regression coefficients with ΔAMOC at 20\u0026deg;S as the predictor and ΔSST as a response variable, show statistically significant negative values (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) over the equatorial and South Atlantic (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). These negative values indicate that the projected slow-down of the AMOC at 20\u0026deg;S can induce an increase in SST. A warming of the tropical Atlantic is consistent with heat convergence from the weakening of the northward heat transport in the Atlantic, as implied by the stronger AMOC weakening at 30\u0026deg;N compared to 20\u0026deg;S (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The correlation between the ΔAMOC and ΔSST among the models in the tropical Atlantic is large and statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with R\u003csup\u003e2\u003c/sup\u003e of 0.4 over all the SAWP (0\u0026deg; to -15\u0026deg;S and 34\u0026deg;W to 20\u0026deg;W). This indicates that around 40% of the uncertainties in projected warming of the tropical Atlantic (40\u0026ndash;5\u0026deg;W and 20\u0026deg;S \u0026minus;\u0026thinsp;5\u0026deg;N) is related to weakening of the AMOC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eSimilar regression analysis for precipitation, ΔPR, indicate that a weakening of AMOC can lead to a southward shift of the Atlantic ITCZ (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), consistent with previous findings \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. This can eventually lead to increased precipitation in the western equatorial Atlantic and eastern part of Brazil, and decreased precipitation in the north equatorial Atlantic, western Brazil and equatorial Africa. ΔAMOC explains up to 40% of the uncertainties in projected precipitation in southern ENEB, while it explains 20% or less of the projected uncertainty in the northern region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eFor a better understanding, we examine these relations using indices. We calculate the SST change over the eastern tropical Atlantic area (ΔTA) (40\u0026deg;\u0026ndash;5\u0026deg;W and 20\u0026deg;S \u0026minus;\u0026thinsp;5\u0026deg;N), where ΔAMOC explains most of the variance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). A strong and statistically significant (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) negative correlation of almost \u0026minus;\u0026thinsp;0.7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec) indicates that the SST over the tropical Atlantic increases as AMOC at 20\u0026deg;S declines. The warming is consistent with accumulating heat content due to a weakened AMOC and northward heat transport in the Atlantic. In some models, the AMOC at 20\u0026deg;S weakens by up to 3 Sv per degree of global warming. According to the regression relation, this weakening contributes an additional\u0026thinsp;~\u0026thinsp;0.2\u0026deg;C warming per degree of global warming in this region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). This is equivalent to a local amplification of global warming by ~\u0026thinsp;30%.\u003c/p\u003e \u003cp\u003ePrevious investigations have established a substantial correlation between SST in the SAWP located in eastern tropical south Atlantic, and precipitation in the ENEB (ΔENEB) \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e],[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e],[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. This is because as Atlantic SST increases in this region, sea level pressure drops, increasing atmospheric moisture, eventually promoting convection \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e],[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. The CMIP6 MME shows that projected changes in precipitation in the ENEB region are similarly related to future SST warming in the tropical Atlantic (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). The ΔENEB has a statistically significant positive correlation to ΔSST of approximately 0.80 (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Although the MME mean indicates a decrease in precipitation in the ENEB by -0.07 mm/day per degree of global warming, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed indicates that whether a model predicts a precipitation increase or decrease in this region depends on the level of SST warming in the tropical Atlantic.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGiven the high correlation between ΔENEB and ΔTA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed) and between ΔAMOC and ΔTA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), we further examine the relationship between precipitation and AMOC changes. The scatter plot between ΔENEB and ΔAMOC illustrates a negative linear relation, with increases in precipitation in ENEB correlated to decreases in AMOC (r\u0026thinsp;~\u0026thinsp;\u0026minus;\u0026thinsp;0.6; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). The negative slope indicates that the AMOC slowdown compensates for decreasing precipitation in the ENEB, by accumulating ocean heat and warming the SAWP. As discussed below, this is associated with increased evaporation from the ocean, which could enhance moisture convergence and eddy moisture transport and thereby increase precipitation \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eLatent Heat Flux changes on AMOC weakening\u003c/h3\u003e\n\u003cp\u003eWe examine the linear relation between latent upward heat flux changes (ΔLHF) and ΔAMOC, from the MME, to better understand the influence on ENEB precipitation, which is related to moisture convergence over the western south tropical Atlantic. AMOC decline in the SSP585 scenario leads to an increase in the LHF (as the regression is negative) in the southern branch of the South Equatorial Current (sSEC), considered a zonal pathway of the AMOC upper branch and the northern limit of the South Atlantic Subtropical Gyre (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The statistically significant (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) increase in LHF in the western south tropical Atlantic is consistent with a weakening-AMOC driven increase in SST in this region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Likewise, in the central south Atlantic Subtropical Gyre, LHF decreases in response to the cooling associated with the weakening AMOC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eIn response to these ocean driven SST changes the ITCZ migrates southward in the Atlantic, consistent with the AMOC-forced ITCZ southward shift in past warmer climates \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. As the ITCZ shifts further south, LHF decreases in the equatorial region and to the north; in this region it appears that a decrease in LHF is associated with weaker winds and tends to warm the ocean \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Simultaneously, the positive ΔLHF to the south will also result in more water vapour convergence over the western south tropical Atlantic. Thus, the AMOC-decline driven increase in heat content of the upper ocean, increase heat fluxes and moisture content of the lower troposphere and shift the ITCZ south, all of which have a statistically significant (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) impact on South American climate. In addition, when the wind is easterly, evaporation in the western south tropical Atlantic is expected to feed diabatic energy to easterly wave disturbances, which induce extreme precipitation in the ENEB \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e],[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e],[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. The increase in precipitation over the land seems to drive the increase of LHF in the ENEB (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eEstimating the influence of AMOC decline on tropical precipitation and SST\u003c/h3\u003e\n\u003cp\u003eMotivated by the robust impacts of AMOC on precipitation and SST over tropical Atlantic and surrounding regions, we now quantify the contribution of AMOC decline to the total projected MME mean changes in tropical Atlantic precipitation and SST. These are compared to the more direct effects of global warming, which causes robust changes in the hydrological cycle \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. We separate the changes conceptually into three parts: one related to the AMOC at 20\u0026deg;S, based on the regressions shown above (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, b); the second is related to GSAT; and the third includes all changes unrelated to either AMOC or GSAT and is assumed to average to zero across the models. After normalising the variables by the ΔGSAT to reduce the impact of different rates of global warming in the different models, we estimate the spatial changes in precipitation and SST using regression analysis. This analysis framework is outline in the Methods section.\u003c/p\u003e \u003cp\u003eThe MME mean projects a uniform and positive ΔSST of up to 0.8\u0026deg;C per degree of global warming (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Of this, global warming (ΔGSAT) contributes directly to an increase of up to 0.7\u0026deg;C in the equatorial region and to the north (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), while the AMOC weakening contributes to a warming around 0.1\u0026deg;C in the equatorial regional and to the south (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). Note the AMOC at 20 \u0026deg;S is projected to decrease by 1.14 Sv/\u0026deg;C. We estimate changes by the end of the century under the SSP5-8.5 scenario, for which the MME mean indicates 3.5\u0026deg;C global warming and 4 Sv reduction in AMOC at 20 \u0026deg;S. For this level of warming and AMOC weakening our analysis indicates a direct global warming contribution of 2.5\u0026deg;C in the tropical Atlantic and an indirect contribution from AMOC weakening of 0.35\u0026deg;C.\u003c/p\u003e \u003cp\u003eRegarding precipitation, the MME mean projects a decrease of 0.1 mm/day per degree of global warming in the north and northeast of Brazil and over the western equatorial Atlantic, while the decrease is much less in the ENEB (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The direct global warming contribution shows a greater decrease in precipitation that exceeds 0.2 mm/day/\u0026deg;C in the western equatorial Atlantic and 0.1 mm/day/\u0026deg;C and in the ENEB (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). This is equivalent to a reduction of ~\u0026thinsp;125 mm/yr per degree Celsius over the ENEB in a 3.5\u0026deg;C warmer world, as projected by the MME mean under the SSP5-8.5. These changes follow the expected dry-get-drier paradigm \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e],[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Consistently, a larger decrease in northeast of Brazil precipitation is found in the high emission-low adaptation scenario (SSP5-8.5) compared to low emission-high adaptation scenarios \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. The reduction in precipitation is particularly significant (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the northeast region\u0026mdash;the driest area in Brazil, which faces substantial climate risks; for example, the Pernambuco State with 89% of its area affected by drought and low precipitation is highly vulnerable to climate change \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e],[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e],[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAMOC-weakening compensates for the direct global warming impact on precipitation over the ENEB and the equatorial Atlantic, while over the north-west region (~\u0026thinsp;10\u0026deg;N-10\u0026deg;S and ~\u0026thinsp;70\u0026deg;W-65\u0026deg;W) it enhances the global warming impact (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). Over the ENEB region, the MME mean AMOC-weakening of 1.1 Sv/\u0026deg;C increases precipitation by ~\u0026thinsp;0.1 mm/day/\u0026deg;C almost exactly balancing the direct global warming contribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed and f). For ΔAMOC of -4 Sv in the MME mean under the SSP5-8.5 scenario, the precipitation is expected to increase by approximately 131 mm per year (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). This value is ~\u0026thinsp;11% of the contribution, compared with the annual average precipitation in the ENEB, which is approximately 1500 mm/y, according to the National Institute of Meteorology (INMET) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.inmet.gov.br\u003c/span\u003e\u003cspan address=\"https://portal.inmet.gov.br\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Over the Amazon region, the AMOC weakening and global warming reduce precipitation almost equally, with the AMOC signal predominant in the west (~\u0026thinsp;60\u0026deg;W). These results are consistent with studies investigating AMOC shutdown experiments \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe AMOC weakening drives precipitation changes in the tropical Atlantic and in the ENEB through the SST changes. The AMOC-weakening warms the equatorial and south Atlantic, inducing a cross-equatorial SST gradient (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). This causes the Atlantic ITCZ to shift south (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef), as its location is closely related to the cross-equatorial SST gradient \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e],[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e],[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. This mechanism is found in model experiments with strong AMOC weakening \u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e],[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. The increased precipitation in the ENEB is connected to the ITCZ southward shift (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). Next, we examine AMOC-weakening impacts on extreme precipitation events in the ENEB, which is vulnerable to such extremes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eExtreme precipitation changes\u003c/h3\u003e\n\u003cp\u003eIn the ENEB, 60% of precipitation comes from mesoscale meteorological systems like Easterly Waves \u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. We identify changes of the extreme indices related to the AMOC at 20\u0026deg;S and global mean surface temperature (GSAT), and we normalize the variables by the ΔGSAT (see Methods). Here we investigate the impact of AMOC decline on extreme precipitation, considering the maximum amount of ΔPR accumulated over one day (ΔRx1day, mm/\u0026deg;C) and over five days (ΔRx5day, mm/\u0026deg;C), and daily precipitation\u0026thinsp;\u0026gt;\u0026thinsp;99th percentile (ΔR99p, mm/\u0026deg;C).\u003c/p\u003e \u003cp\u003eThe extreme precipitation indices are similarly correlated to ΔAMOC (Fig. S3), although with varying degrees of intensity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A weakening AMOC leads to substantial increase in the precipitation extremes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed-f) over the Amazon, northeast, and southeast of Brazil as well as the center of SEC path and close to the Agulhas Current system. The changes in the ΔRx5day are more considerable over the Amazon. Therefore, we now estimate how much the AMOC reduction contribute to precipitation-extreme indices over Brazil and contrast them to the direct consequences of global warming, under the SSP5-8.5 scenario (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe MME mean projects a similar pattern of change for all three extremes precipitation indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-c) per degree of global warming, but with different magnitude. There is an increase of ~\u0026thinsp;3 mm/\u0026deg;C in ΔRx1day (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea) and ~\u0026thinsp;5mm/\u0026deg;C in ΔRx5day (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), and ~\u0026thinsp;20 mm/\u0026deg;C in ΔR99p (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec) over the north part and northeast of Brazil, and north equatorial Atlantic. There is good agreement with more than 75% of the models having the same sign of change over the north and northeast of Brazil.\u003c/p\u003e \u003cp\u003eConsidering the direct global warming contribution, there is an increase in extremes precipitation over most of Brazil, except for a small region in the north (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed-f). The ΔRx1day (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed) and ΔRx5day (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee) show a decrease of over ~ -3 mm/\u0026deg;C in the north of Brazil. On the other hand, there are increases up 3 to 5 mm/\u0026deg;C over the southeast of the country (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed-e), and ~\u0026thinsp;10 mm/\u0026deg;C for the ΔR99p (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef) over the northeast of Brazil with high agreement among the models. This is equivalent to an increase of ~\u0026thinsp;18 mm in ΔRx5day (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee) over the northern part of the ENEB and southeast of Brazil in a 3.5\u0026deg;C warmer world, as projected by the MME mean under the SSP5-8.5 scenario. We observe an even stronger increase in the northwest of Africa with precipitation extremes expected to double. These changes are consistent with the wet-get-wetter paradigm under climate change \u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAMOC-weakening drives a substantial increase in accumulated extreme precipitation in the north and northeast of Brazil, while the intensity increases in the north and decreases in the southwest part of Amazon. The increase of ~\u0026thinsp;1 mm/ \u0026deg;C in ΔRx1day (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg), ~ 5 mm/ \u0026deg;C in ΔRx5day (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh) and ~\u0026thinsp;10 mm/ \u0026deg;C in ΔR99p (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei) over north of Brazil compensates the decrease in extreme precipitation caused from the direct global warming part. For ΔAMOC of -4 Sv in the MME mean under the SSP5-8.5 scenario, the ΔRx5day increases up to 14 mm (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh) over the ENEB and 70 mm in north of Brazil. AMOC-weakening contributes to drier conditions (~\u0026thinsp;5 mm/ \u0026deg;C) conditions over the southwest and wetter extreme conditions (~\u0026thinsp;20 mm/ \u0026deg;C) in the north of the Amazon, 10 mm/ \u0026deg;C in the northeast, as well as in the ENEB where there is an increase\u0026thinsp;~\u0026thinsp;5 mm/ \u0026deg;C for ΔR99p (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei). Like the mean precipitation changes, a weakened AMOC influence on extreme precipitation is of similar magnitude to the global warming impact and can even dominate the response over North Brazil.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe investigated how a possible future AMOC decline (defined as CMIP6 historical minus SSP5-8.5 simulations) modulates the tropical Atlantic SST, LHF, mean precipitation and precipitation extremes, by analyzing MME of CMIP6 models. As previous studies have shown, global warming is projected to bring warmer and drier conditions in north and northeast Brazil \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e],[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. The main effect of an AMOC weakening is to warm tropical Atlantic SST (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, c and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee) and increase LHF, leading to wetter conditions in the ENEB and drier conditions in the north of Brazil (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). Thus, a weakened AMOC partially offsets the drier conditions expected from global warming in northeast Brazil and amplifies the drier and warmer scenario over northern Brazil, especially in the Amazon. We showed the role of the ocean in driving precipitation changes, in contrast to previous studies that focused on changes in the atmospheric poleward heat transport \u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur new findings on rainfall extremes are another novel aspect, which has revealed the intricate influences of AMOC decline on hydrometeorological climate in Brazil. Global warming is expected to increase extreme precipitation over most of Brazil. The AMOC weakening in some regions and for some extreme indices amplifies the global warming signal, while in others it offsets them: In the northwest of Brazil, the AMOC weakening intensifies extreme precipitation (R99p, Rx1day and Rx5day), while global warming reduces extremes in this region. In the ENEB, AMOC weakening intensifies the Rx5day and R99p, but compensates the Rx1day. Interestingly, in some regions the response of the precipitation extremes to the weakening AMOC are like that found for mean precipitation (e.g., ENEB for the Rx5day and R99p) while in other regions it is the opposite. The similar response of mean and extreme precipitation in the ENEB to the AMOC weakening is associated with the southward-shifted ITCZ and local ocean warming that leads to the wetter conditions, favoring the instability of meteorological systems \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, and the intensification of the easterly waves \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e],[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. The opposite response of AMOC weakening on the mean and extreme precipitation in the northwest is like that of global warming, which can lead to increased temperatures and altered precipitation patterns.\u003c/p\u003e \u003cp\u003eThe impact of the AMOC weakening on the extreme and mean precipitation in the north of Brazil and on mean precipitation in the ENEB are robust, while the impact on extreme precipitation in the ENEB is less substantial. The robust impacts are associated with large-scale features, such as the southward ITCZ shift. The uncertainty for the ENEB could be linked to small-scale processes that are poorly resolved in climate models, such as easterly wave disturbances. This is also reflected in the inaccurate simulation of the seasonal rainfall cycle by the models, which typically show the maximum precipitation in February\u0026ndash;March rather than the observed peak between May and July (Fig. S2). Despite these errors and uncertainties, an increase in precipitation extremes over the ENEB is expected due to regional SST warming in the western equatorial Atlantic, associated with AMOC weakening. These results suggest that the potential impact of global warming on severe precipitation events may be underestimated in the ENEB. This is a concern for the already vulnerable ENEB region where the extreme events are projected to increase. Further tools for investigation, such as CORDEX-South America (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cordex.org/domains/cordex-region-south-america-cordex/\u003c/span\u003e\u003cspan address=\"https://cordex.org/domains/cordex-region-south-america-cordex/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), could help understand model uncertainty.\u003c/p\u003e \u003cp\u003eWhile global warming exerts drastic regional effects over Brazil, where the ENEB and the Amazon are at increasing risk of dryness, the AMOC change can substantially modulate these impacts. Our results show that the AMOC weakening can partially offset the projected drying in the ENEB while intensifying it in northern Brazil, especially in the Amazon region. Regarding the extreme precipitation, the AMOC acts as a compensating mechanism for climate change, adding complexity and uncertainty to future projections. In the ENEB region this modulation may result in more frequent events. These findings underscore the importance of reducing uncertainties related to the AMOC's response to global warming to better constrain regional climate projections and inform effective adaptation strategies in vulnerable areas.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003cp\u003eCMIP6 Data\u003c/p\u003e\n \u003cp\u003eWe use MME of CMIP6 simulations \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e following the historical and SSP5-8.5 scenarios \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e, which is a high-end forcing scenario with a global mean radiative forcing of 8.5 W m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e by 2100 and low climate change adaptation strategies. We use the AMOC, SST, precipitation, global surface air temperature and surface upward latent heat flux data from 29 CMIP6 models (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e in Supporting Information S1). The data is at monthly temporal resolution. The datasets, except for AMOC, are interpolated to a common 1\u0026deg; \u0026times; 1\u0026deg; grid before analysis. Only one ensemble member for each model is used. For most models we selected member r1i1p1f1, but for 11 models, the r1i1p1f1 was not available when we accessed the database, hence we use other ensemble members (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e in Supporting Information S1). For the analysis of precipitation extremes indices based on daily precipitation rates, we had access to data from 22 of the 29 models (Table S2 in Supporting Information S1).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003cp\u003eFramework for Assessing Changes in Climate Indices\u003c/p\u003e\n \u003cp\u003eOur analysis focuses on the projected changes from 2050 to 2100 (SSP5-8.5) relative to 1950 to 2000 (for historical). We compute the change in a variable, V, for a particular model, m, as follows:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cp class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere the overbar represents an average over the different 50-year periods, and the \u0026Delta;\u003cem\u003eV\u003c/em\u003e represents the change of the variable.\u003c/p\u003e\n \u003cp\u003eWe analyze the following variables: the AMOC_20\u0026deg;S and AMOC_30\u0026deg;N indices, computed as the maximum of the ocean meridional overturning stream function at 20\u0026deg;S (the closest to 34\u0026deg;S found for most models) and at 30\u0026deg;N in the Atlantic for each year; The SST mean index (\u0026Delta;TA) for the tropical Atlantic (40\u0026ndash;5\u0026deg;W and 20\u0026deg;S \u0026minus;\u0026thinsp;5\u0026deg;N), as well as the precipitation mean index (\u0026Delta;PR) for the ENEB region (36\u0026ndash;34\u0026deg;W and 10\u0026ndash;6\u0026deg;S) and the latent upward heat flux changes (\u0026Delta;LHF). We examine the relationship between AMOC indices and each variable.\u003c/p\u003e\n \u003cp\u003eWe also analyze annual mean precipitation and Climpact indices (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://climpact-sci.org/indices/\u003c/span\u003e\u003c/span\u003e) of annual total precipitation from very wet days (R99p), monthly maximum 1-day (Rx1day) and 5-day precipitation amount (Rx5day)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. R99p is defined as the annual sum of days when the precipitation exceeds the 99th percentile of wet-day precipitation amounts, with the percentile calculated over the baseline period 1981\u0026ndash;2010 (Text S1 in Supporting Information S1).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003cp\u003eIsolating AMOC influences from other global warming impacts\u003c/p\u003e\n \u003cp\u003eWe use output from the MME to estimate the influences of AMOC (denoted \u0026Delta;A\u003csub\u003em\u003c/sub\u003e) decline and other global warming (\u0026Delta;G\u003csub\u003em\u003c/sub\u003e) influences on SST, mean and extremes precipitation assuming the following decomposition:\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003cp\u003e\u0026Delta;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{V}\\varvec{V}}_{\\varvec{m}}\\left(\\varvec{x},\\varvec{y}\\right)=\\:\\)\u003c/span\u003e\u003c/span\u003e\u0026Delta;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\varvec{A}}_{\\varvec{m}}\\:{\\varvec{P}}_{\\varvec{A}}\\left(\\varvec{x},\\varvec{y}\\right)+{{{\\Delta\\:}\\varvec{G}}_{\\varvec{m}}\\varvec{P}}_{\\varvec{G}}\\left(\\varvec{x},\\varvec{y}\\right)+{\\varvec{Z}}_{\\varvec{m}}(\\varvec{x},\\varvec{y})\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eWhere \u0026Delta;\u003cem\u003eVV\u003c/em\u003e represents the decomposition variable (PR, SST, Rx1day, Rx5day and R99p), subscript m denotes a specific model, and \u0026Delta;Z\u003csub\u003em\u003c/sub\u003e independent of \u0026Delta;G\u003csub\u003em\u003c/sub\u003e and \u0026Delta;A\u003csub\u003em\u003c/sub\u003e. In the following we neglect \u0026Delta;Zm, assuming that there are no other systematic influences on \u0026Delta;PR, \u0026Delta;SST, \u0026Delta;Rx1day and \u0026Delta;Rx5day and \u0026Delta;R99p, while this assumption is questionable, the overall high explained variances of our results suggest it is reasonable. P\u003csub\u003eA\u003c/sub\u003e and G\u003csub\u003eA\u003c/sub\u003e are the spatial patterns associated with a unit change in AMOC and global warming.\u003c/p\u003e\n \u003cp\u003e\u0026Delta;G\u003csub\u003em\u003c/sub\u003e is defined as the change in global mean surface air temperature. The CMIP models simulate a wide range of global warming, as they have diverse climate sensitivity. The large spread makes it difficult to isolate the direct impact of AMOC on SST, mean and extremes precipitation as they are strongly influenced by global warming. To reduce this confounding influence, we divide each term in Eq. (2) by \u0026Delta;G\u003csub\u003em\u003c/sub\u003e (*) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003cp\u003e\u0026Delta;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\varvec{V}\\varvec{V}}_{\\varvec{m}}\\left(\\varvec{x},\\varvec{y}\\right)}^{\\varvec{*}}={\\:{{\\Delta\\:}\\varvec{A}}_{\\varvec{m}}}^{\\varvec{*}}{\\varvec{P}}_{\\varvec{A}}\\left(\\varvec{x},\\varvec{y}\\right)+{\\varvec{P}}_{\\varvec{G}}\\left(\\varvec{x},\\varvec{y}\\right)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(3\\right)\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eHere, we redefined separately the variables:\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cp class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:{\\Delta\\:}{{\\varvec{V}\\varvec{V}}_{\\varvec{m}}\\left(\\varvec{x},\\varvec{y}\\right)}^{\\varvec{*}}=\\frac{{\\Delta\\:}{\\varvec{V}\\varvec{V}}_{\\varvec{m}}\\left(\\varvec{x},\\varvec{y}\\right)}{{\\Delta\\:}{\\varvec{G}}_{\\varvec{m}}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(4\\right)\\:$$\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cp class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$\\:{{\\Delta\\:}\\:\\varvec{A}\\varvec{m}}^{\\varvec{*}}\\:=\\frac{{\\Delta\\:}{\\varvec{A}}_{\\varvec{m}}}{{\\Delta\\:}{\\varvec{G}}_{\\varvec{m}}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(5\\right)\\:$$\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eWe estimate the spatial patterns using regression analysis across the ensemble of different models.\u003c/p\u003e\n \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026lt;\\dots\\:\u0026gt;m\\)\u003c/span\u003e\u003c/span\u003e is the covariance computed across the models.\u003c/p\u003e\n \u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n \u003cp class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e$$\\:{\\varvec{P}}_{\\varvec{A}}\\left(\\varvec{x},\\varvec{y}\\right)=\\frac{{{\u0026lt;{\\Delta\\:}\\:{\\varvec{V}\\varvec{V}}_{\\varvec{m}}}^{\\varvec{*}}\\:{{\\Delta\\:}\\:{\\varvec{A}}_{\\varvec{m}}}^{\\varvec{*}}\u0026gt;}_{\\varvec{m}}\\:}{{{\u0026lt;{\\Delta\\:}\\:{\\varvec{A}}_{\\varvec{m}}}^{\\varvec{*}}\\:{{\\Delta\\:}\\:{\\varvec{A}}_{\\varvec{m}}}^{\\varvec{*}}\u0026gt;}_{\\varvec{m}}\\:}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(6\\right)\\:$$\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eFrom this equation, we estimate the change in SST, mean and extremes precipitation due to the mean AMOC change:\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cp class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:{{\\Delta\\:}\\:{\\varvec{V}\\varvec{V}}_{\\varvec{A}}\\left(\\varvec{x},\\varvec{y}\\right)}^{\\varvec{*}}=(\\frac{1}{\\varvec{M}}{\\sum\\:}_{\\varvec{i}=1}^{\\varvec{M}}\\left({{\\Delta\\:}\\:\\:{\\varvec{A}}_{\\varvec{i}}}^{\\varvec{*}}\\right){)\\varvec{P}}_{\\varvec{A}}\\left(\\varvec{x},\\varvec{y}\\right)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:$$\u003c/p\u003e\n \u003cp class=\"EquationNumber\"\u003e7\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1}{\\varvec{M}}{\\sum\\:}_{\\varvec{i}=1}^{\\varvec{M}}\\left({{\\Delta\\:}\\:\\:{\\varvec{A}}_{\\varvec{i}}}^{\\varvec{*}}\\right)\\:\\:\\:\\)\u003c/span\u003e\u003c/span\u003eis the model mean change in AMOC (Sv) for 1-degree Celsius change in global mean temperature.\u003c/p\u003e\n \u003cp\u003eThen we can estimate the change in SST, mean and extremes precipitation for 1-degree Celsius change in global mean temperature:\u003c/p\u003e\n \u003cdiv id=\"Eque\" class=\"Equation\"\u003e\n \u003cp class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e$$\\:{\\varvec{P}}_{\\varvec{G}}\\left(\\varvec{x},\\varvec{y}\\right)=\\frac{1}{\\varvec{M}}{\\sum\\:}_{\\varvec{i}=1}^{\\varvec{M}}\\left({{\\Delta\\:}\\:{\\varvec{V}\\varvec{V}}_{\\varvec{i}}\\left(\\varvec{x},\\varvec{y}\\right)}^{\\varvec{*}}\\right)-\\:{{\\Delta\\:}\\:{\\varvec{V}\\varvec{V}}_{\\varvec{A}}\\left(\\varvec{x},\\varvec{y}\\right)}^{\\varvec{*}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(8\\right)\\:$$\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eThe first term on the right is the multimodel mean.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003cp\u003eStatistical tests\u003c/p\u003e\n \u003cp\u003eWe compute the regression coefficients (slopes), Spearman rank correlation \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e and statistical metrics (R\u0026sup2; and p-values) to understand consistency and divergence across simulations. In estimating statistical significance, we assume independence of each model. Therefore, we evaluated CMIP6 (Text S1 in Supporting Information S1) model agreement using ensemble spread analysis.\u003c/p\u003e\n\u003c/div\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\n\u003cp\u003eThe CMIP6 data used in this study is available from ESGF (https://esgf-index1.ceda.ac.uk/search/cmip6-ceda/) with references listed in Supporting Information S1 (Table S1).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCode Availability \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAvailable on request to the corresponding author.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI. Vilela would like to thank the Funda\u0026ccedil;\u0026atilde;o de Amparo \u0026agrave; Ci\u0026ecirc;ncia e Tecnologia do Estado de Pernambuco (FACEPE) (Grant IBPG-1210-1.08/18) and CAPES-PRINT (Grant 88887.717529/2022-00). I. V. thanks to the Partnership for Observation of the Global Ocean (POGO) and the Scientific Committee on Oceanic Research (SCOR) for the fellowship awarded. All authors are grateful to the TRIATLAS Project, which has received funding from the European Union\u0026apos;s Horizon 2020 research and innovation program under Grant Agreement Number 817578. I. V. and D. V. thank the SIMOPEC Project, funded by the Brazilian National Research Council (CNPq), Grant Number 406707/2022 and the Project Climate analysis and\u003cbr\u003e coupled ocean-atmosphere modeling of extreme events in response to warming in the\u003cbr\u003e Brazil-Malvinas Confluence Region - CNPq Grant 421049/2023-5. S. K. is supported by TRIATLAS project (EU 2020 Horizon, grant # 817578) and EUREC4A-OA project funded by the Research Council of Norway (Grant Number: 317267) under a joint JPI Climate and JPI Ocean call). N.K. and S. K. thank the Research Council of Norway SFI Climate Futures (Gant Number: 309562)\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFinancial support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research has been supported by SIMOPEC Project, funded by the Brazilian National Research Council (CNPq), Grant Number 406707/2022.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e \u003c/p\u003e\n\u003cp\u003eI. V. prepared the AMOC, global surface air temperature, sea surface temperature, mean precipitation, latent heat flux datasets, conceived the methods, performed the analyses, created the figures, and wrote the first paper draft. N. K. contributed to the methods and verified the figures and conclusions. P. D. L. prepared the mean and extreme precipitation datasets. All authors contributed to the interpretation of the results and edited the manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence\u003c/strong\u003e and requests for materials should be addressed to I. Vilela, P. De Luca, S. Koseki, T. Silva, D. Veleda, N. Keenlyside.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBuckley, M. W. \u0026amp; Marshall, J. Observations, inferences, and mechanisms of the Atlantic meridional overturning circulation: \u003cem\u003ea review. Rev. 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Indices for monitoring changes in extremes based on daily temperature and precipitation data. \u003cem\u003eWIREs Clim Change\u003c/em\u003e \u003cstrong\u003e2,\u003c/strong\u003e 851-870. https://doi.org/10.1002/wcc.147(2011).\u003c/li\u003e\n\u003cli\u003eSpearman, C. \u0026ldquo;The Proof and Measurement of Association between Two Things.\u0026rdquo; \u003cem\u003eThe American Journal of Psychology\u003c/em\u003e \u003cstrong\u003e100,\u003c/strong\u003e no. 3/4 (1987): 441\u0026ndash;71. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-climate-and-atmospheric-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjclimatsci","sideBox":"Learn more about [npj Climate and Atmospheric Science](http://www.nature.com/npjclimatsci/)","snPcode":"41612","submissionUrl":"https://submission.springernature.com/new-submission/41612/3","title":"npj Climate and Atmospheric Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6933933/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6933933/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlobal warming is expected to substantially weaken the Atlantic Meridional Overturning Circulation (AMOC). However, climate models disagree greatly on the magnitude of AMOC weakening due to the large uncertainties in climate change projections, especially in the tropics. Here, we show through multi-model analysis of (CMIP6) future climate change projections that AMOC weakening during the next century will strongly influence precipitation and its extremes over Brazil. Such weakening dominates over the direct global warming impacts, causing drying in the Amazon, while completely mitigating them in northeast Brazil. We trace this to a tropical Atlantic warming, consistent with weakened heat transport along the southern branch of the South Equatorial Current. This induces a cross-equatorial sea surface temperature gradient and changes in latent heat flux, shifting the intertropical convergence zone southward. Our findings highlight the need to reduce uncertainties in the AMOC response to global warming and its oceanic mediated influences on Brazilian climate.\u003c/p\u003e","manuscriptTitle":"AMOC Weakening Dominates Global Warming Impacts on Precipitation Over Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-23 12:37:26","doi":"10.21203/rs.3.rs-6933933/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-10T00:52:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-09T15:06:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-09T02:19:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273992177567090502422631897962432697536","date":"2025-06-25T13:12:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"83731168043983925352187146612074196269","date":"2025-06-23T12:09:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122002426560989659781795470076077683745","date":"2025-06-21T00:38:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-20T12:36:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-20T10:11:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-20T10:06:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Climate and Atmospheric Science","date":"2025-06-19T23:03:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-climate-and-atmospheric-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjclimatsci","sideBox":"Learn more about [npj Climate and Atmospheric Science](http://www.nature.com/npjclimatsci/)","snPcode":"41612","submissionUrl":"https://submission.springernature.com/new-submission/41612/3","title":"npj Climate and Atmospheric Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"af0661d5-29e3-41d7-815b-30c545430927","owner":[],"postedDate":"June 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":50410598,"name":"Earth and environmental sciences/Climate sciences"},{"id":50410599,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate change impacts"}],"tags":[],"updatedAt":"2025-11-24T16:09:35+00:00","versionOfRecord":{"articleIdentity":"rs-6933933","link":"https://doi.org/10.1038/s41612-025-01248-w","journal":{"identity":"npj-climate-and-atmospheric-science","isVorOnly":false,"title":"npj Climate and Atmospheric Science"},"publishedOn":"2025-11-17 15:57:31","publishedOnDateReadable":"November 17th, 2025"},"versionCreatedAt":"2025-06-23 12:37:26","video":"","vorDoi":"10.1038/s41612-025-01248-w","vorDoiUrl":"https://doi.org/10.1038/s41612-025-01248-w","workflowStages":[]},"version":"v1","identity":"rs-6933933","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6933933","identity":"rs-6933933","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}