Coastal El Niño and La Niña Events in a Changing Climate: Insights from the CESM2 Large Ensemble

Coastal El Niño and La Niña Events in a Changing Climate: Insights from the CESM2 Large Ensemble | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Coastal El Niño and La Niña Events in a Changing Climate: Insights from the CESM2 Large Ensemble Leandra Grisel Loyola, Cristian Martinez-Villalobos, Boris Dewitte This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7774655/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 5 You are reading this latest preprint version Abstract Coastal El Niño events in the Eastern Boundary Upwelling system off Peru have garnered significant attention due to their substantial societal impacts. The recent events, 2017 and 2023, rank amongst the strongest on record, raising concerns about their future behavior. This study relies on the CESM2 Large Ensemble (CESM2-LE) to explore how the frequency, intensity and spatial patterns of coastal events may evolve throughout the 21st century. Initially, an evaluation of the model revealed a pattern bias associated with a too energetic South Pacific Meridional Mode (SPMM) and a weaker North Pacific Meridional Mode (NPMM), both patterns known to affect coastal warming. Nevertheless, the model realistically simulates precipitation during coastal events in both their cold and warm phases and captures a strong link to Pacific Meridional Modes (PMMs). At the end of the 21st century, warm coastal events are expected to become 40% less frequent but are associated with a precipitation increase of approximately 2 mm/day due to increased sea surface temperatures in the mean state. Future climatological precipitation levels during February-March-April (FMA) from the third decade of the 21st century onward are projected to match those currently seen during extreme events, such as the 2017 Coastal El Niño episode. Coastal La Niña, conversely, exhibits no meaningful change in frequency or intensity, but may serve as intervals of moderate rather than extreme precipitation in the future. Coastal El Niño climate change Global Climate Models Marine Heat Waves Tropical Pacific Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Among Tropical Pacific Climate phenomena, the El Niño Southern Oscillation (ENSO) stands out as one of the most significant drivers of global climate variability (Horel & Wallace, 1981; McPhaden et al., 2006). ENSO exerts widespread impacts globally (Jeon et al., 2025), including climate impacts, ecosystem disruptions and fishery production (Cai et al., 2020; Taschetto et al., 2020), making it one of the most extensively studied climate phenomena. Of particular concern is how ENSO will evolve in warmer climate (Cai et al., 2021). ENSO shows two distinct variants characterized by differing spatial patterns and underlying physical mechanisms and teleconnections. The most extensively studied ENSO variant is characterized by sea surface temperature (SST) anomalies in the far eastern Pacific, known as the Eastern Pacific El Niño, which can sometimes reach extreme intensity (Takahashi et al., 2011). In contrast, the Central Pacific El Niño is marked by weaker SST anomalies centered in the central equatorial Pacific. The recognition of these two event types has led to the concept of ENSO diversity (Capotondi et al., 2015). Recent research has focused on determining whether this diversity reflects distinct dynamical regimes or arises from different external forcing conditions (Timmermann et al., 2018). Moreover, ENSO has been linked to an increase in the frequency of marine heatwaves across the Pacific (Gregory et al., 2024). In contrast to basin-scale ENSO events, a distinct type —Coastal El Niño— has recently emerged. It is characterized by a warming of SST along the coasts of Peru and Ecuador during the late austral summer, while the central equatorial Pacific remains in a cool or near-neutral state (Garreaud, 2018; Lübbecke et al., 2019; Peng et al., 2024; Takahashi & Martínez, 2019). This warming is driven by a local Bjerknes feedback, in which an initial SST anomaly is amplified through a local atmospheric response involving northerly winds off Ecuador and a southward shift of the Intertropical Convergence Zone (ITCZ) (Takahashi & Martínez, 2019). The initial SST anomaly may be triggered either by intraseasonal oceanic Kelvin wave activity (Echevin et al., 2018; Lübbecke et al., 2019; Peng et al., 2024) or by remote atmospheric disturbances that affect coastal upwelling (Garreaud, 2018; Martinez-Villalobos et al., 2024; Wei et al., 2025). This type of marine heat wave has gained considerable scientific attention in 2017 following an extreme event that triggered intense rainfall and widespread societal disruptions in Peru and Ecuador that could not be predicted (Yglesias-González et al., 2023). The 2017 event in Peru resulted in losses of approximately $US 3.100 million (Garreaud, 2018), not including the damage to over 60,000 hectares of agricultural land according to the United Nations (United Nations News, 2017) and the subsequent surge in dengue cases with around 50.000 infections according to the Dirección General de Epidemología del Perú (MINSA, 2025) (see also Yglesias-González et al., (2023)). More recently, a comparable coastal event occurred in 2023, characterized by even stronger SST anomalies and more intense precipitation (Martínez-Villalobos et al., 2024; Peng et al., 2024; Tan et al., 2024). This latter event further evolved as a basin-scale El Niño, which was not the case of the 2017 Coastal El Niño event, which has raised concern whether Coastal El Niño can influence Tropical Pacific variability (Tan et al., 2024). Moreover, Coastal El Niño events have been shown to be linked to the Pacific Meridional Modes (PMMs) and to have a cold counterpart, the Coastal La Niña (Martínez-Villalobos et al., 2024), thus sharing key characteristics with basin-scale El Niño events. Coastal El Niño events present a significant challenge for prediction systems due to their rapid evolution and relatively weak persistence in the central Pacific compared to their basin-scale counterpart (Ramírez & Briones, 2017; Rivera Tello et al., 2023). They have received limited attention, largely because of sparse coverage in the far eastern Pacific (east of 95°W) and because current generation coupled climate models lack the resolution needed to realistically capture coastal upwelling dynamics. They also exhibit a persistent warm bias in the far eastern Pacific (Geng & Jin, 2023; Luo et al., 2017; Smith et al., 2019) that also may influence Coastal El Niño dynamics and forcing mechanism. Nevertheless, long-term integrations of climate models offer a valuable resource for their study. Recently Rudloff & Lübbecke (2025) used the CESM1 Large Ensemble (Kay et al., 2015) to investigate the drivers of Coastal El Niño events, focusing on features that can make them evolve as basin-scale event. Here we use the next generation of this model resource, the CESM2 Large ensemble (Rodgers et al., 2021), to also characterize Coastal El Niño drivers focusing on the PMMs. A large ensemble gives us a great opportunity to identify changes on the events internal variability and to assess the model performance (Deser, 2020). Considering their societal impacts, our interest is also to evaluate how CESM2 Large Ensemble projects precipitations during Coastal El Niño in the future climate. Future increases in local SSTs may in particular enhance the strength of the regional Bjerknes feedback (Fu & Fedorov, 2023), potentially favoring the development of Coastal El Niño events and associated precipitation. However, this effect could be modulated by a concurrent rise in the convective threshold associated with a more stratified tropical troposphere (Johnson & Xie, 2010). In addition, key drivers of Coastal El Niño variability—such as the PMMs and the Madden–Julian Oscillation (MJO)—are also expected to exhibit changes under global warming, with implications for the frequency, timing, and spatial expression of these events (Liguori & Di Lorenzo, 2019; Wang et al., 2022). Given the complex interplay of ocean–atmosphere interactions governing Coastal El Niño events, the use of a large ensemble provides a robust framework for assessing future changes in their dynamics. The ensemble’s extended temporal coverage and ensemble spread also enable a more rigorous analysis of internal variability (Maher et al., 2023; Rudloff & Lübbecke, 2025). Our analysis thus provides both insights into the mechanisms of Coastal El Niño and an assessment of the CESM2 Large Ensemble ability to simulate such events, contributing to the growing body of literature on this unprecedented modeling resource. The structure of this paper is as follows: Section 2 outlines the method, detailing the datasets used and statistical approach to evaluating the model. Section 3 presents the results, with emphasis on model performance, the relationship between PMMs and coastal events, and the projections of future precipitation associated with these events. Finally, Section 4 provides a discussion of the findings and their broader implications. 2. Methodology 2.1. Data Monthly SST, precipitation and surface wind data from the CESM2 Large Ensemble (hereafter CESM2-LE) (Rodgers et al., 2021 ) are used. Model outputs were interpolated to a regular horizontal grid of 1° by 1°. Two scenarios are used (historical and SSP 3.7) allowing a comparison of two periods of 85 years each: 1929–2014 and 2015–2100; 100 members are used resulting in 8500 years of data for each period. To compare model results with observed data we used the NOAA Extended Reconstruction SST v5 reanalysis (Huang et al., 2017 ) to obtain monthly SST data at a resolution of 2.5° by 2.5°. Furthermore, we used the Global Precipitation Climatology Project dataset (Adler et al., 2003 ) for monthly precipitation data and the NCEP-NCAR reanalysis 1 (Kalnay et al., 1996 ) for monthly surface wind data, both on a 2.5° by 2.5° grid. For all reanalysis data we used a period from 1980 to 2024. 2.2. Indices and Coastal El Niño classification Prior to analysis, we calculated anomalies and 3-month running means across all data. Anomalies in the reanalysis data were computed by subtracting the mean seasonal cycle to the data calculated over the period 1980–2024, followed by a smoothing process using the first two Fourier harmonics and subsequently linear detrending. For the CESM2-LE, the anomalies were determined by subtracting the time-evolving ensemble mean amongst the 100 available members. Afterward, a 3-month running mean was calculated on the anomalies. The application of the running mean was applied to focus on events during February-March-April (FMA), which is the season in which warming/cooling maximizes the magnitude of associated precipitation anomalies. We classified the events by establishing a threshold for the Niño 1 + 2 index (SST anomalies average over 0°-10°S, 90°W-80°W) and Niño 3.4 index (SST anomalies average over 5°N-5°S,170°W-120°W) (Trenberth, 1997 ) during the months of FMA. These months were selected due to their alignment with the rainfall season along the South Pacific coast (Garreaud, 2018 ). It is during this season that minor fluctuations in SST are more likely to result in extreme precipitation (Martinez-Villalobos et al., 2024 ). We classify coastal events as follows: Coastal El Niño events : Niño 1 + 2 index in FMA > 0.3°C and Niño 3.4 index in FMA < -0.3°C. Coastal La Niña events : Niño 1 + 2 index in FMA -0.3°C Setting the threshold at 0.3°C strikes a balance between capturing meaningful SST anomalies and including enough events for statistically robust composites. PMMs indices in this study were computed following the methodology described in Martínez-Villalobos et al. (2024). As in Chiang and Vimont ( 2004 ), we adjusted the monthly anomalies of SST and wind using the Niño 3.4 index to remove the basin-wide El Niño signal. Subsequently, we performed a maximum covariance analysis (MCA) (Bretherton et al., 1992 ) on the adjusted SST and wind data to identify coupled patterns and obtain the South Pacific Meridional Mode (SPMM) (40°S–0°S, 140°W–90°W) (Dewitte et al., 2023 ) and North Pacific Meridional Mode (NPMM) (21°S–32°N, 175°E–95°W) indices. A 3-month running mean was applied to the PMMs indices afterward. To visualize the patterns associated with the indices (PMMs patterns), we regressed in each grid point the SST and wind (with a 3-month running mean) onto the SPMM and NPMM index separately (Fig. 3 ). Interestingly, the SPMM signal, which is most prominent during the FMA season, is largely captured by the second EOF mode of SST anomalies over the tropical Pacific in the model. The time-series correlation between the SPMM and PC2 indices is r = 0.84 for the historical scenario and r = 0.71 for the future scenario (Supplementary Fig. 3), highlighting the significant influence of the SPMM in the model. Notably, in the large ensemble, PC2 is represented by PC3, due to its closer resemblance to PC2 of the observed data (0.72 spatial correlation coefficient) (Supplementary Figs. 5 and 6), suggesting that the SPMM in the model essentially took over the role of PC2, displacing the analogous of PC2 in observations to the third position. As detailed previously, the analysis of PMMs variability required isolating the NPMM from the dominant SPMM signal, which was captured by the first principal component. Consequently, the second principal component was used as a proxy for the NPMM, consistent with the approach outlined in Richter et al. ( 2022 ) (see Supplementary Fig. 8). In our study, we applied the aforementioned methodology for all datasets, utilizing the more dominant mode of the MCA for both hemispheres, except for the CESM2-LE for which the second mode is used for the NPMM and the first mode for the SPMM. This decision was based on the selection of the pattern that most closely resembled and had the highest correlation to the reanalysis PMM patterns (0.81 and 0.79 correlation coefficient for SPMM and NPMM) (see Supplementary Fig. 8). 2.3. Model evaluation and statistical analysis We assessed events spatial patterns using composite analysis (Fig. 1 ) and calculated composite features that are symmetric between Coastal El Niño and La Niña events (Supplementary Fig. 2) by subtracting the values of the cold phase from the warm phase and dividing the result by two. Conversely, for antisymmetric composite features are determined by adding these values and dividing the sum by two. The same methodology was used for the model and the reanalysis data. To analyze the evolution of coastal events in the CESM2-LE and its relationship with PMMs, we inspect the composite evolutions of the PMMs and Niño 1 + 2 indices along with their dispersion diagnosed from the 31-year running mean and standard deviation (Fig. 5 ). The relationship between precipitation and SST is also analyzed as a function of calendar month and amongst events. The climatology for the model was calculated using all simulations, for the historical record (1929–2014) and the future record (2015–2100). Climatology for observed data was derived using the span of the record (1980–2024) (Fig. 4). Additionally, we calculated risk ratios to compare historical and future precipitation, which we explain in detail in the next sub-section (Fig. 6). Finally, spatial correlations of coastal event composites and symmetric features between CESM2-LE and reanalysis data were computed over the entire tropical Pacific, in the region of 20°S-20°N, 120°E-170°W. The same domain was used to calculate spatial correlations for SST regressions onto Niño indices. 2.4. Risk ratios Risk Ratios (RR) are used to estimate changes in probability of occurrence of an event given a certain threshold (Stott et al., 2004 ; Martinez-Villalobos & Neelin, 2018 ). In this study, RRs are used to evaluate the probability of precipitation exceeding various percentiles, comparing future and historical records. We also compared precipitation conditions during coastal events in contrast with normal conditions (months without anomalies associated with coastal and basin-wide El Niño events). RR were calculated as follows: $$\:RR=\frac{\raisebox{1ex}{${n}_{1}$}\!\left/\:\!\raisebox{-1ex}{${N}_{1}$}\right.}{\raisebox{1ex}{${n}_{2}$}\!\left/\:\!\raisebox{-1ex}{${N}_{2}$}\right.}$$ 1 Here, \(\:{n}_{1}\) and \(\:{n}_{2}\) denote the number of events exceeding a given precipitation threshold under historical and future conditions, respectively, while \(\:{N}_{1}\) and \(\:{N}_{2}\) are the total number of events for each period. This gives us the ratio of the precipitation probability that Coastal El Niño events in the future can be associated with relatively to that of an event in the contemporary climate (historical record). 3. Results 3.1. Coastal events patterns and model evaluation In this section we evaluate the skill of the CESM2-LE in simulating Coastal El Niño events. Overall, the model simulates lower frequency of occurrences of warm events relative to cold events in both the historical and future runs, when compared to observational data. In the historical record, Coastal El Niño events constitute 18% of the total events, significantly lower than the 42% identified (6 of a total of 14 coastal events) in the reanalysis data. Additionally, Coastal El Niño events decrease their frequency by 40% in the model future scenario, while Coastal La Niña events do not have a significant change in their frequency. In terms of spatial patterns, Coastal El Niño events in the CESM2-LE historical simulations exhibit positive SST anomalies that extend both centrally and southward, extending as far as central Chile and reaching beyond the Niño 3.4 region (Fig. 1c). This westward extension has been previously observed in CMIP5 models, when simulating basin-wide El Niño events (Luo et al., 2017). Compared to observational data (Fig. 1a), the model simulates a more pronounced southern extension, with a spatial correlation between composite maps only reaching 0.50 correlation coefficient in the 20°S-20°N, 160°E-70°W band. Similarly, the composite pattern for Coastal La Niña events in the CESM2- LE historical run shows negative SST anomalies extending southward and westward from the Niño 1+2 region (Fig. 1d) more in the model than in the observations. The composite SST anomaly pattern correlation reaches 0.70 in the 20°S-20°N, 160°E-70°W band. Although the model tends to simulate spatially broader SST anomaly patterns than those observed, it successfully captures the main characteristics of coastal warming events. Its performance is comparable to that of the previous CESM version (CESM1) (Karamperidou & DiNezio, 2022; Rudloff & Lübbecke, 2025). However, coastal events in CESM1 typically exhibit a temporal lag, with SST anomalies peaking during April–June (AMJ), as opposed to the February–April (FMA) peak period in the observed data (Rudloff & Lübbecke, 2025b). The CESM2-LE does present this lag but not in the majority of events, specifically, the peak of Coastal El Niño events distributes from February to June (Supplementary Fig. 9). Wind anomalies associated with coastal events are also generally well represented in the CESM2-LE simulations. Northerly wind anomalies along the equator and a weakening of the easterly trades frequently accompanied by westerly wind anomalies observed during warm Coastal El Niño events (Rodríguez-Morata et al., 2019), are present in the simulated Coastal El Niño events in the historical scenario (Fig. 1c). On the other hand, Coastal La Niña events in the model exhibit anomalous wind patterns marked by a predominantly northward and westward flow (Fig. 1c), consistent with the expected atmospheric circulation associated with cold anomalies along the southeastern Pacific coast (Martinez-Villalobos et al., 2024). The composite precipitation anomaly pattern simulated by CESM2-LE exhibits notable similarity to observations, with a pronounced precipitation maximum centered over the Niño 1+2 region (Fig. 2d). Nevertheless, the model reproduces an eastward extension of the anomaly field, compared to observations, reaching approximately 145°W. The spatial correlation between modeled and observed precipitation composites within the 20°S–20°N, 160°E-70°W band is r = 0.51. For Coastal La Niña events precipitation pattern, the model demonstrates comparable skill, with a spatial correlation of 0.43. Additionally, the model’s precipitation climatology time-series for the FMA season shows a high correlation with observations (r = 0.98), albeit with a positive bias of approximately 1 mm/day (Fig. 4a). Notably, although the model simulates higher precipitation totals during FMA, the sensitivity of precipitation to SST anomalies is greater in observations. Specifically, precipitation increases by 0.9 mm/day per 1°C SST increase in the model, while observed data indicate a stronger response of 1.4 mm/day per 1°C (Fig. 4c). This indicates that while the model captures the large-scale relationship between SST and rainfall, it underestimates the strength of the observed precipitation response in the Niño 1+2 region. When considering all calendar months, CESM2-LE accurately captures the overall relationship between SST and precipitation anomalies (Fig. 4b). However, for Coastal La Niña events, the model exhibits a marked positive precipitation bias, characterized by substantially higher precipitation values (Fig. 4c) despite simulating SST anomalies of similar magnitude to those observed (Fig. 4d). In contrast, Coastal El Niño events show good agreement with observations in terms of both absolute precipitation and SST anomaly amplitudes (Figs. 4c and 4d). The excessive rainfall during La Niña events suggests a positive bias in the model’s precipitation response to SST cooling. To further investigate the structural features contributing to these biases, we decompose the composite SST anomalies into their symmetric and anti-symmetric components. This decomposition reveals that the model replicates the large-scale structure of both components with moderate skill, yielding spatial correlation coefficients of r=0.57 for symmetric and r=0.60 for antisymmetric features (Supplementary Figs. 2a and 2b) within the 20°S–20°N, 160°E-70°W band. These results indicate that while CESM2-LE broadly captures the spatial organization of coastal SST anomalies, subtle asymmetries and pattern extensions—particularly during La Niña phases—may contribute to the discrepancy between model and observation. Symmetric characteristics are predominantly concentrated in the Niño 1+2 region and exhibit a southward extension toward the Niño 3.4 region in both the historical and future simulations (Supplementary Figs. 2d and 2f). While this general pattern is also evident in the observations, the spatial extent in the observed data does not reach into the Niño 3.4 region. This indicates that the model successfully captures part of the characteristics associated with the dynamics of both coastal warm and cold phases, with a more pronounced representation of antisymmetric dynamics. The antisymmetric features are predominantly concentrated in the Niño 1+2 and Niño 3.4 regions, including the dipole that extends from the north-central Pacific to the southern Niño 1+2 region. This pattern is more clearly defined in the model simulations, where it becomes apparent that coastal warm and cold events exhibit distinct spatial structures, with no evident pattern similarity in this region. In contrast to the observed data, the CESM2-LE demonstrates a strong correlation between the antisymmetric and symmetric features with PMMs. The symmetric features in the historical record exhibit a spatial correlation coefficient of 0.81, with their corresponding SPMM pattern within the 40°S–0°N, 160°E-70°W band. Conversely, the NPMM shows an inverse correlation with the antisymmetric features, with a correlation coefficient of -0.37. This inverse relationship is not observed in observation data. These similarities imply that, within the CESM2-LE, the SPMM may play a key role in modulating the spatial pattern and temporal evolution of Coastal El Niño events, whereas the NPMM appears to govern processes that distinguish warm from cold phases. However, it is important to note that SPMM and NPMM exhibit reduced statistical independence in CESM2-LE compared to observations, often manifesting as opposing patterns, which may limit their distinct interpretability in the model. Finally, in terms of the Niño indices, the model represents correctly the spatial regressions of the Niño 1+2 and Niño 3.4 indices compared with observed data (Supplementary Fig. 1), although we can note a bias in SST anomalies with a westward extension of the pattern. This increased intensity of the model representation of the indices can be also observed in the relationship between the indices with a correlation between the Niño 1+2 index and the Niño 3.4 index of r=0.87, compared to a r=0.64 correlation coefficient in observations. 3.2. Relationship with Pacific Meridional Modes As outlined in the introduction, PMMs have recently been implicated in both the enhancement and initiation of Coastal El Niño events (Martínez-Villalobos et al., 2024). Accordingly, evaluating the representation of PMMs within the model is essential for interpreting the spatial expression and projected evolution of coastal climate variability. To evaluate the influence of PMMs on coastal variability, we first examine the relationship between the PMMs indices and the Niño 1+2 index. As illustrated in Supplementary Figure 4, the NPMM and Niño 1+2 indices during FMA are in fact independent (r=0.08 and -0.09 for the historical and SSP 3.7 scenario respectively), whereas in observations, they are somewhat linearly related to r=0.39 (statistically significant at the 95% level). In contrast, the SPMM displays a similar level of association with the Niño 1+2 index across both model simulations and observational records (Supplementary Figs. 4a and 4b) (r=0.39 and r=0.37, for the model historical record and observations, respectively), suggesting a more systematic linkage. Analysis of composite Coastal El Niño events reveals however a notable correspondence between modeled SST anomalies and PMMs patterns. In the CESM2-LE historical simulation, the spatial correlation between coastal warm events composites and the SPMM reaches 0.73 within the 40°S–0°N, 160°E-70°W band (Fig. 3c), increasing substantially to 0.86 under future forcing conditions (Fig. 3e). By comparison, the observed spatial correlation is lower, at 0.32 (Fig. 3a). This alignment is reflected in the model’s pronounced extension of coastal warming anomalies eastward and southward from the Niño 1+2 region (Figs. 1c and 1e), closely resembling the SPMM structure depicted in Figures 3c and 3e. Coastal La Niña events (Figs. 1.d and 1.f) show similar behavior. They exhibit strong inverse correlations with the SPMM pattern, (r=-0.75 historical and r=-0.78 future, within the 40°S–0°N, 160°E-70°W band), reflecting similar southward extensions as in observed warm events. This contrasts with the spatial correlation of r=-0.07 between observed cold coastal events (Fig. 1b) and SPMM. These findings highlight a notable bias in the PMMs influence structure within the model. Unlike the reanalysis data, where NPMM is more dominant than tSPMM (e.g., Martinez-Villalobos et al., 2024), the model exhibits a particularly strong SPMM signal in coastal events, while NPMM has less influence on coastal events than its observed counterpart. This PMMs pattern bias is also reflected in precipitation. We observed a composite of precipitation anomalies when the SPMM index is above 1, that strongly resembles the precipitation composite of Coastal El Niño events, likewise for Coastal La Niña events but with opposite sign for the SPMM index (see Supplementary Figures 7c and 7e). This highlights that the influence of the SPMM in coastal events in the model does not just limit to their SST anomalies, but it is involved in the intensity of precipitation too. 3.3. Coastal Events Precipitation Precipitation is the most consequential aspect of Coastal El Niño events and thus the central variable examined in this study. As demonstrated earlier, precipitation is accurately captured by the CESM2-LE, exhibiting low bias and correctly simulated patterns, along with precipitation linked to the SPMM. We now examine how precipitation associated with coastal events is projected to evolve under future climate conditions. Projections indicate a general increase in precipitation of approximately 2 mm day⁻¹ during the FMA period under future climate conditions (Fig. 4a), with localized maxima reaching up to 5 mm day⁻¹ in association with elevated SSTs approaching 34 °C (Fig. 4b). In the context of future coastal events, 18% of Coastal La Niña episodes are characterized by precipitation anomalies below –2 mm day⁻¹ (Fig. 4d), in stark contrast to only 0.3% of such events in the model historical record (Fig. 4c). These findings point to a marked intensification of precipitation deficits associated with cold-phase coastal events under future warming conditions. However, as shown in Figure 4c, the absolute precipitation values during future Coastal La Niña events are projected to exceed those in the model’s historical baseline. This implies that, despite the enhanced intensity of cold events SST anomalies, the increase in mean-state precipitation under warming scenarios could result in Coastal La Niña episodes that produce more rainfall than their historical counterparts. Although peak precipitation anomalies between the historical and future records show no substantial differences, elevated future SST leads to an overall increase in FMA precipitation during both Coastal El Niño and Coastal La Niña events. A significant proportion of future events will exceed the precipitation levels observed during the 2017 (Echevin et al., 2018) and 2023 (Hu et al., 2019) events—approximately 4 mm/day (Fig. 6c)—with 38.7% of total events surpassing this threshold, including some Coastal La Niña events, despite stronger cold anomalies projected for cold events in future projections (Fig. 4d). For Coastal El Niño events, although SST and precipitation anomalies remain within the same range as in the historical period, 51% of future warm events are projected to reach precipitation levels comparable to those recorded during the extreme basin-wide El Niño events of 1982–83 and 1997–98 (Martinez-Villalobos et al., 2024), with precipitation exceeding 7 mm/day. This is further quantified in Fig. 6, which shows through a risk ratio that the probability of future Coastal El Niño events exceeding the 2017 and 2023 events precipitation is approximately 10 times higher than in the historical record. For Coastal La Niña events, the risk ratio rises to nearly 100 for future events to precipitate more than their historical counterparts (Fig. 6b). This is consistent with Fig. 6c and the finding that the risk of future Coastal La Niña events producing below-normal precipitation is lower than that of historical cold events doing so. In summary, coastal event precipitation in CESM2-LE appears increasingly driven by externally forced changes in the mean climate state. While internal variability remains important, the dominant signal in future scenarios is one of enhanced precipitation —even during cold events— driven by elevated SST. This could reduce the likelihood of truly dry Coastal La Niña periods and reshape how such events affect regional impacts. 4. Discussion and concluding remarks Understanding and projecting the behavior of Coastal El Niño events remains a major challenge in climate science. Their rapid onset, short persistence, and sensitivity to regional processes distinguish them from basin-wide events, complicating both prediction and model evaluation. In this study, we assessed how CESM2-LE simulates the characteristics and projected changes of Coastal El Niño and La Niña events under a high-emissions scenario, with a particular focus on SST patterns, PMMs dynamics, and precipitation impacts. From this analysis we can highlight three main findings: The overly active SPMM in the CESM2-LE and its influence in the performance of the model in simulating Coastal events. The decrease in frequency of Coastal El Niño events but clear increase in their intensity in terms of precipitation. The potential role of future Coastal La Niña events in providing temporary windows of moderated SST and precipitation anomalies under otherwise extreme conditions driven by anthropogenic warming. 4.1. SPMM in the CESM2-LE and its role for Coastal El Niño events. In terms of the evaluation of the model in simulating Coastal El Niño events, we found important structural biases in the CESM2-LE. The model reproduces the basic SST-precipitation relationships and large-scale PMMs patterns reasonably well, but it overemphasizes the SPMM and underrepresents the NPMM, especially during the FMA season. This imbalance affects both the spatial structure of coastal events and their classification in principal component space, with PC2 in the model dominated by the SPMM. Although CESM2-LE captures the observed correlation between Niño 1 + 2 SST and SPMM variability, it fails to reproduce the strength of the NPMM connection. These biases may limit the realism of the dynamics that shape event diversity and evolution. To further understand this structural problem, we investigate the long-term covariability between PMMs and coastal events Niño 1 + 2 index values to assess their potential role in modulating multidecadal changes. Notably, Fig. 5 a highlights a strong correlation (r = 0.88) between the 31-year running mean standard deviation of the SPMM and the Niño 1 + 2 index, indicating a tight coupling between low-frequency SPMM variability and Niño 1 + 2 region activity. This suggests that variability in the Niño 1 + 2 region during FMA is linked to the variability of the SPMM during the same period. For coastal events, we find that Coastal El Niño episodes are consistently associated with positive values of the SPMM index, while Coastal La Niña events correspond to negative SPMM index values throughout the 1929–2100 period (Fig. 5 b). We observe a significant 86% of Coastal El Niño events associated with positive SPMM instances and 77% of Coastal La Niña events associated with negative SPMM instances, highlighting a possible role of the SPMM as precursor and enhancer of coastal events. Additionally, this symmetry reinforces the hypothesis that SPMM variability influences the occurrence of both warm and cold coastal events. The concurrent variability in both amplitude and frequency of SPMM and Coastal El Niño/La Niña events over the 1929–2100 period suggests a robust, low-frequency coupling between subtropical and eastern Pacific variability. The systematic alignment in sign, variability strength, and event occurrence across decades supports the hypothesis that SPMM dynamics play a central role in modulating the occurrence and characteristics of coastal events in the CESM2-LE. This relationship, while not perfectly linear or phase-synchronized, implies a common background modulation or a nonlinear interaction pathway that may enhance predictability of coastal climate anomalies on decadal timescales. 4.2. Coastal Events in the future In contrast to basin-wide El Niño events, the projection of coastal events poses a significant challenge for climate models, as evidenced by the performance of CESM2-LE in this study. Nonetheless, the model outputs yield valuable insights into the dynamics and predictability of such events. The model projects a substantial decline in the frequency of Coastal El Niño events during the 21st century, with an estimated reduction of approximately 40% relative to the previous century. On the other hand, Coastal La Niña events do not exhibit a significant change between the present and future climates, although they represent more than 80% of total events. Despite this, our analysis reveals a robust increase in precipitation associated with coastal events in the future, driven primarily by warmer SSTs. Notably, 52% of future warm events exceed the maximum historical precipitation threshold of 6.8mm/day. Even cold events, historically associated with suppressed rainfall (e.g., Martinez-Villalobos et al., 2024 ), show enhanced precipitation —suggesting that future Coastal La Niña events may resemble historically neutral or even wet conditions. This intensification is quantified using risk ratios (Fig. 6), which show that the probability of extreme precipitation in future Coastal La Niña events is up to 100 times greater than in Coastal La Niña events during the historical record. Such findings suggest that externally forced climate change, rather than internal variability, is becoming the dominant driver of coastal event impacts. Even considering the projected intensification of mean-state SST and precipitation conditions in the Niño 1 + 2 region by the third decade of the 21st century, Coastal El Niño events will continue to pose a significant hazard. Model projections indicate that these events will produce precipitation rates exceeding climatological averages in the future, with magnitudes surpassing those observed in the recent events of 2017 and 2023. As illustrated in Fig. 6b, the associated risk ratio suggests an increased likelihood of Coastal El Niño events yielding above-average rainfall, thereby amplifying the severity of their impacts relative to their historical counterparts. Despite the structural biases in PMMs representation, notably an overly energetic SPMM and an underrepresented NPMM, CESM2-LE captures key features relevant to coastal events impacts. Its precipitation response to SPMM forcing closely matches observational patterns (Supplementary Fig. 7), and the model reproduces the strong linkage between coastal SST anomalies and rainfall in the Niño 1 + 2 region. These strengths suggest that, even with limitations in the simulation of the underlying modes, CESM2-LE provides valuable insights into how Coastal El Niño and La Niña events may evolve in a warming climate. 4.3. Coastal La Niña as Climatic Interludes Between Extreme Phases While the impacts of Coastal El Niño events have received considerable attention in the literature, Coastal La Niña events remain comparatively understudied. In this work, we show that CESM2-LE projects no statistically significant changes in future coastal cold events. As illustrated in Fig. 4c, although future Coastal La Niña events are projected to display stronger cold SST anomalies, the concurrent increase in mean-state precipitation and SST reduces their relative intensity compared to historical counterparts. Thus, while rainfall anomalies may become more negative, the higher baseline means absolute precipitation often exceeds historical Coastal La Niña totals. In fact, these future events are expected to be associated with higher SST and rainfall than those observed during historical cold episodes, so their roles as “windows of relief” should be understood relative to even wetter future El Niño conditions, not relative to the past. This shift presents a potential opportunity; under a warming and wetter climate regime, Coastal La Niña events may offer transient periods of reduced precipitation extremes. As shown in Fig. 6b, the likelihood that these events will produce below-normal rainfall in the future scenario, creating windows of relative atmospheric and oceanic stability. Notably, 64% of all FMA periods in the future scenario are projected to exceed the rainfall observed during the 2017 event, with precipitation exceeding 7 mm/day, underscoring the importance of the role that Coastal La Niña events might have in the future. Coastal El Niño events have disproportionate impacts on society, particularly in Peru and Ecuador, where fisheries, infrastructure, and public health are highly vulnerable to changes in rainfall (Yglesias-González et al., 2023 ). The 2017 and 2023 events, though driven by different precursors (Hu et al., 2019; Peng et al., 2024 ), each caused widespread damage, highlighting the diverse origins of these events and the limits of current predictive systems. As this study shows, even cold events may no longer provide relief from heavy rainfall, and that preparedness efforts should account for a broader range of extreme outcomes. In summary, CESM2-LE successfully captures the fundamental SST–precipitation relationship associated with coastal events, yet exhibits structural biases—namely, an overly energetic SPMM and a dampened NPMM —that influence the spatial patterns and variability of these events. Under the SSP 3.7 scenario, projections indicate that mean-state warming exerts a dominant influence: warm coastal events become less frequent (40% decline), while FMA precipitation increases by approximately 2 mm day⁻¹, and the probability of extreme rainfall rises markedly across both warm and cold phases. Future warm events frequently reach precipitation levels comparable to historical extremes, whereas cold events unfold within a wetter climatological background, often resembling present-day neutral-to-wet conditions. This suggests a diminished likelihood of truly dry Coastal La Niña episodes. These findings underscore the central role of mean-state changes in shaping future impacts along the Peru–Ecuador coast and highlight the importance of improving PMMs representation and near-coastal process dynamics in climate models to enhance predictive skill and risk management. Declarations Data availability NOAA Extended Reconstruction SST v5 reanalysis can be accessed at https://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.html. The Global Precipitation Climatology Project dataset can be accessed at https://psl.noaa.gov/data/gridded/data.gpcp.html. The NCEP-NCAR reanalysis v1 can be accessed at https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html. Data from the CESM2 Large Ensemble can be accessed at https://www.cesm.ucar.edu/community-projects/lens2/data-sets. Code availability All codes used to perform the corresponding analysis are available from the corresponding author on reasonable request. Acknowledgements The authors acknowledge the CESM2 Large Ensemble Community Project and supercomputing resources provided by the IBS Center for Climate Physics in South Korea. BD acknowledges supports from ANID (Concurso de Fortalecimiento al Desarrollo Científico de Centros Regionales 2020-R20F0008-CEAZA, COPAS COASTAL FB210021 and Fondecyt Regular 1231174). C.M.-V. acknowledges support from Proyecto ANID Fondecyt Regular, Proyecto ANID Iniciación 11250471 and Data Observatory Foundation ANID Technology Center number DO210001. Author contributions All authors contributed to the study conception and design. Data collection and analyses was performed by Leandra Loyola, with assistance by Cristian Martínez-Villalobos and Boris Dewitte. Leandra Loyola wrote the first draft, and all authors contributed with consecutive versions of the manuscript. All authors read and approved the final manuscript. Competing interests The authors declare no competing interests. References Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., & Nelkin, E. (2003). The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–Present). Journal of Hydrometeorology, 4(6), 1147-1167. https://doi.org/10.1175/1525-7541(2003)0042.0.CO;2 Bretherton, C. S., Smith, C., & Wallace, J. M. (1992). An Intercomparison of Methods for Finding Coupled Patterns in Climate Data. Journal of Climate, 5(6), 541-560. https://doi.org/10.1175/1520-0442(1992)0052.0.CO;2 Cai, W., McPhaden, M. J., Grimm, A. M., Rodrigues, R. R., Taschetto, A. S., Garreaud, R. D., Dewitte, B., Poveda, G., Ham, Y., Santoso, A., Ng, B., Anderson, W., Wang, G., Geng, T., Jo, H., Marengo, J. A., Alves, L. M., Osman, M., Li, S., . . . Vera, C. (2020). Climate impacts of the El Niño–Southern Oscillation on South America. Nature Reviews Earth & Environment, 1(4), 215-231. https://doi.org/10.1038/s43017-020-0040-3 Cai, W., Santoso, A., Collins, M., Dewitte, B., Karamperidou, C., Kug, J., Lengaigne, M., McPhaden, M. J., Stuecker, M. F., Taschetto, A. S., Timmermann, A., Wu, L., Yeh, S., Wang, G., Ng, B., Jia, F., Yang, Y., Ying, J., Zheng, X., . . . Zhong, W. (2021). Changing El Niño–Southern Oscillation in a warming climate. Nature Reviews Earth & Environment, 2(9), 628-644. https://doi.org/10.1038/s43017-021-00199-z Capotondi, A., Wittenberg, A. T., Newman, M., Di Lorenzo, E., Yu, J., Braconnot, P., Cole, J., Dewitte, B., Giese, B., Guilyardi, E., Jin, F., Karnauskas, K., Kirtman, B., Lee, T., Schneider, N., Xue, Y., & Yeh, S. (2014). Understanding ENSO diversity. Bulletin Of The American Meteorological Society, 96(6), 921-938. https://doi.org/10.1175/bams-d-13-00117.1 Chiang, J. C. H., & Vimont, D. J. (2004). Analogous Pacific and Atlantic Meridional Modes of Tropical Atmosphere–Ocean Variability*. Journal Of Climate, 17(21), 4143-4158. https://doi.org/10.1175/jcli4953.1 Deser, C. (2020). “Certain Uncertainty: The Role of Internal Climate Variability in Projections of Regional Climate Change and Risk Management”. Earth S Future, 8(12). https://doi.org/10.1029/2020ef001854 Dewitte, B., Concha, E., Saavedra, D., Pizarro, O., Martinez-Villalobos, C., Gushchina, D., Ramos, M., & Montecinos, A. (2023). The ENSO-induced South Pacific Meridional mode. Frontiers In Climate, 4. https://doi.org/10.3389/fclim.2022.1080978 Echevin, V., Colas, F., Espinoza-Morriberon, D., Vasquez, L., Anculle, T., & Gutierrez, D. (2018). Forcings and Evolution of the 2017 Coastal El Niño Off Northern Peru and Ecuador. Frontiers In Marine Science, 5. https://doi.org/10.3389/fmars.2018.00367 Fu, M., & Fedorov, A. (2023). The Role of Bjerknes and Shortwave Feedbacks in the Tropical Pacific SST Response to Global Warming. Geophysical Research Letters, 50(19). https://doi.org/10.1029/2023gl105061 Garreaud, R. D. (2018). A plausible atmospheric trigger for the 2017 coastal El Niño. International Journal Of Climatology, 38(S1). https://doi.org/10.1002/joc.5426 Geng, T., Jia, F., Cai, W., Wu, L., Gan, B., Jing, Z., Li, S., & McPhaden, M. J. (2023). Increased occurrences of consecutive La Niña events under global warming. Nature, 619(7971), 774-781. https://doi.org/10.1038/s41586-023-06236-9 Gregory, C. H., Artana, C., Lama, S., León‐FonFay, D., Sala, J., Xiao, F., Xu, T., Capotondi, A., Martinez‐Villalobos, C., & Holbrook, N. J. (2024). Global Marine Heatwaves Under Different Flavors of ENSO. Geophysical Research Letters, 51(20). https://doi.org/10.1029/2024gl110399 Horel, J. D., & Wallace, J. M. (1981). Planetary-Scale Atmospheric Phenomena Associated with the Southern Oscillation. Monthly Weather Review, 109(4), 813-829. https://doi.org/10.1175/1520-0493(1981)1092.0.CO;2 Huang, B., Thorne, P. W., Banzon, V. F., Boyer, T., Chepurin, G., Lawrimore, J. H., Menne, M. J., Smith, T. M., Vose, R. S., & Zhang, H. (2017). Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal Of Climate, 30(20), 8179-8205. https://doi.org/10.1175/jcli-d-16-0836.1 Hu, Z., Huang, B., Zhu, J., Kumar, A., & McPhaden, M. J. (2018). On the variety of coastal El Niño events. Climate Dynamics, 52(12), 7537-7552. https://doi.org/10.1007/s00382-018-4290-4 Jeon, Y., Kim, S., Sohn, S., Han, S., & Moon, S. (2025). Toward a Sustainable and Resilient Society through Enhanced ENSO Response and Preparedness. Bulletin Of The American Meteorological Society, 106(4), E571-E575. https://doi.org/10.1175/bams-d-25-0024.1 Johnson, N. C., & Xie, S. (2010). Changes in the sea surface temperature threshold for tropical convection. Nature Geoscience, 3(12), 842-845. https://doi.org/10.1038/ngeo1008 Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., & Joseph, D. (1996). The NCEP/NCAR 40-Year Reanalysis Project. Bulletin of the American Meteorological Society, 77(3), 437-472. https://doi.org/10.1175/1520-0477(1996)0772.0.CO;2 Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G., Arblaster, J. M., Bates, S. C., Danabasoglu, G., Edwards, J., Holland, M., Kushner, P., Lamarque, J., Lawrence, D., Lindsay, K., Middleton, A., Munoz, E., Neale, R., Oleson, K., . . . Vertenstein, M. (2014). The Community Earth System Model (CESM) Large Ensemble Project: A Community Resource for Studying Climate Change in the Presence of Internal Climate Variability. Bulletin Of The American Meteorological Society, 96(8), 1333-1349. https://doi.org/10.1175/bams-d-13-00255.1 Karamperidou, C., & DiNezio, P. N. (2022). Holocene hydroclimatic variability in the tropical Pacific explained by changing ENSO diversity. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-34880-8 Liguori, G., & Di Lorenzo, E. (2019). Separating the North and South Pacific Meridional Modes Contributions to ENSO and Tropical Decadal Variability. Geophysical Research Letters, 46(2), 906-915. https://doi.org/10.1029/2018gl080320 Lübbecke, J. F., Rudloff, D., & Stramma, L. (2019). Stand‐Alone Eastern Pacific Coastal warming events. Geophysical Research Letters, 46(21), 12360-12367. https://doi.org/10.1029/2019gl084479 Luo, J., Liu, G., Hendon, H., Alves, O., & Yamagata, T. (2017). Inter-basin sources for two-year predictability of the multi-year La Niña event in 2010–2012. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-01479-9 Maher, N., Wills, R. J., DiNezio, P., Klavans, J., Milinski, S., Sanchez, S. C., Stevenson, S., Stuecker, M. F., & Wu, X. (2023). The future of the El Niño–Southern Oscillation: using large ensembles to illuminate time-varying responses and inter-model differences. Earth System Dynamics, 14(2), 413-431. https://doi.org/10.5194/esd-14-413-2023 Martinez-Villalobos, C., Dewitte, B., Garreaud, R. D., & Loyola, L. (2024). Extreme coastal El Niño events are tightly linked to the development of the Pacific Meridional Modes. Npj Climate And Atmospheric Science, 7(1). https://doi.org/10.1038/s41612-024-00675-5 Martinez‐Villalobos, C., & Neelin, J. D. (2018). Shifts in Precipitation Accumulation Extremes During the Warm Season Over the United States. Geophysical Research Letters, 45(16), 8586-8595. https://doi.org/10.1029/2018gl078465 McPhaden, M. J., Zebiak, S. E., & Glantz, M. H. (2006). ENSO as an Integrating Concept in Earth Science. Science, 314(5806), 1740-1745. https://doi.org/10.1126/science.1132588 MINSA. (2025). Sala situacional de enfermedades metaxénicas. Dirección General de Epidemiología (DGE), Ministerio de Salud del Perú, Retrieved July 2, 2025, from https://app7.dge.gob.pe/maps/sala_metaxenica/ Peng, Q., Xie, S., Passalacqua, G. A., Miyamoto, A., & Deser, C. (2024). The 2023 extreme coastal El Niño: Atmospheric and air-sea coupling mechanisms. Science Advances, 10(12). https://doi.org/10.1126/sciadv.adk8646 Ramírez, I. J., & Briones, F. (2017). Understanding the El Niño Costero of 2017: The Definition Problem and Challenges of Climate Forecasting and Disaster Responses. International Journal Of Disaster Risk Science, 8(4), 489-492. https://doi.org/10.1007/s13753-017-0151-8 Richter, I., Stuecker, M. F., Takahashi, N., & Schneider, N. (2022). Disentangling the North Pacific Meridional Mode from tropical Pacific variability. Npj Climate And Atmospheric Science, 5(1). https://doi.org/10.1038/s41612-022-00317-8 Rodgers, K. B., Lee, S., Rosenbloom, N., Timmermann, A., Danabasoglu, G., Deser, C., Edwards, J., Kim, J., Simpson, I. R., Stein, K., Stuecker, M. F., Yamaguchi, R., Bódai, T., Chung, E., Huang, L., Kim, W. M., Lamarque, J., Lombardozzi, D. L., Wieder, W. R., & Yeager, S. G. (2021). Ubiquity of human-induced changes in climate variability. Earth System Dynamics, 12(4), 1393-1411. https://doi.org/10.5194/esd-12-1393-2021 Rodríguez-Morata, C., Díaz, H. F., Ballesteros-Canovas, J. A., Rohrer, M., & Stoffel, M. (2018). The anomalous 2017 coastal El Niño event in Peru. Climate Dynamics, 52(9-10), 5605-5622. https://doi.org/10.1007/s00382-018-4466-y Rudloff, D., & Lübbecke, J. F. (2025). What Controls the Evolution of Pacific Coastal Niño Events in the CESM Large Ensemble? Geophysical Research Letters, 52(9). https://doi.org/10.1029/2024gl113706 Smith, N., Kessler, W. S., Cravatte, S., Sprintall, J., Wijffels, S., Cronin, M. F., Sutton, A., Serra, Y. L., Dewitte, B., Strutton, P. G., Hill, K., Gupta, A. S., Lin, X., Takahashi, K., Chen, D., & Brunner, S. (2019). Tropical Pacific Observing System. Frontiers In Marine Science, 6. https://doi.org/10.3389/fmars.2019.00031 Stott, P. A., Stone, D. A., & Allen, M. R. (2004). Human contribution to the European heatwave of 2003. Nature, 432(7017), 610-614. https://doi.org/10.1038/nature03089 Takahashi, K., & Martínez, A. G. (2017). The very strong coastal El Niño in 1925 in the far-eastern Pacific. Climate Dynamics, 52(12), 7389-7415. https://doi.org/10.1007/s00382-017-3702-1 Takahashi, K., Montecinos, A., Goubanova, K., & Dewitte, B. (2011). ENSO regimes: Reinterpreting the canonical and Modoki El Niño. Geophysical Research Letters, 38(10), n/a. https://doi.org/10.1029/2011gl047364 Tan, W., Hu, Z., McPhaden, M. J., Zhu, C., Li, X., & Liu, Y. (2024). On the Divergent Evolution of ENSO After the Coastal El Niños in 2017 and 2023. Geophysical Research Letters, 51(9). https://doi.org/10.1029/2024gl108198 Taschetto, A. S., Ummenhofer, C. C., Stuecker, M. F., Dommenget, D., Ashok, K., Rodrigues, R. R., & Yeh, S. (2020). ENSO Atmospheric Teleconnections. Geophysical Monograph, 309-335. https://doi.org/10.1002/9781119548164.ch14 Tello, G. A. R., Takahashi, K., & Karamperidou, C. (2023). Explained predictions of strong eastern Pacific El Niño events using deep learning. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-45739-3 Timmermann, A., An, S., Kug, J., Jin, F., Cai, W., Capotondi, A., Cobb, K. M., Lengaigne, M., McPhaden, M. J., Stuecker, M. F., Stein, K., Wittenberg, A. T., Yun, K., Bayr, T., Chen, H., Chikamoto, Y., Dewitte, B., Dommenget, D., Grothe, P., . . . Zhang, X. (2018). El Niño–Southern Oscillation complexity. Nature, 559(7715), 535-545. https://doi.org/10.1038/s41586-018-0252-6 Trenberth, K. E. (1997). The Definition of El Niño. Bulletin of the American Meteorological Society, 78(12), 2771-2778. https://doi.org/10.1175/1520-0477(1997)0782.0.CO;2 United Nations News. (2017, May 19). Peru: UN agency supports recovery of some 7,000 farmers from El Niño floods, landslides. https://news.un.org/en/story/2017/05/557772 Wang, J., Kim, H., & DeFlorio, M. J. (2022). Future Changes of PNA-like MJO Teleconnections in CMIP6 Models: Underlying Mechanisms and Uncertainty. Journal Of Climate, 35(11), 3459-3478. https://doi.org/10.1175/jcli-d-21-0445.1 Wei, Y., Mu, M., Lian, T., Hsu, H., Ren, H., Wang, J., Ji, C., & Tao, L. (2025). The March 2023 MJO and Its Impacts on the Subsequent Coastal El Niño. Journal Of Climate, 38(4), 1155-1173. https://doi.org/10.1175/jcli-d-24-0198.1 Yglesias-González, M., Valdés-Velásquez, A., Hartinger, S. M., Takahashi, K., Salvatierra, G., Velarde, R., Contreras, A., María, H. S., Romanello, M., Paz-Soldán, V., Bazo, J., & Lescano, A. G. (2023). Reflections on the impact and response to the Peruvian 2017 Coastal El Niño event: Looking to the past to prepare for the future. 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1","display":"","copyAsset":false,"role":"figure","size":2997814,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eReanalysis data Coastal El Niño events composite pattern of SST and wind anomalies from Reanalysis data. \u003cstrong\u003eb\u003c/strong\u003e Same as (\u003cstrong\u003ea\u003c/strong\u003e) but for Coastal La Niña events. \u003cstrong\u003ec \u003c/strong\u003eCESM2-LE historical record Coastal El Niño events composite pattern of SST and wind anomalies. \u003cstrong\u003ed\u003c/strong\u003e Same as (\u003cstrong\u003ec\u003c/strong\u003e) but for Coastal La Niña events. \u003cstrong\u003ee\u003c/strong\u003e CESM2-LE SSP 3.7 record Coastal El Niño events composite pattern of SST and wind anomalies.\u003cstrong\u003e f\u003c/strong\u003e Same as (\u003cstrong\u003ee\u003c/strong\u003e) but for Coastal La Niña events. The green boxes indicate the Niño 1+2 and Niño 3.4 regions, from right to left, respectively. The legend indicates the total number of events identified in each dataset, while hatched areas denote regions of statistical significance, as determined through bootstrap resampling.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7774655/v1/6731714333e00ace12f7dc64.png"},{"id":94851451,"identity":"76285246-77a7-418f-853f-ae2cdfa43d90","added_by":"auto","created_at":"2025-10-31 11:11:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2030314,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eReanalysis data Coastal El Niño events composite pattern of precipitation anomalies. \u003cstrong\u003eb\u003c/strong\u003e Same as (\u003cstrong\u003ea\u003c/strong\u003e) but for Coastal La Niña events. \u003cstrong\u003ec\u003c/strong\u003eCESM2-LE historical record Coastal El Niño events composite pattern of precipitation anomalies. \u003cstrong\u003ed \u003c/strong\u003eSame as (\u003cstrong\u003ec\u003c/strong\u003e) but for Coastal La Niña events. \u003cstrong\u003ee\u003c/strong\u003e CESM2-LE SSP 3.7 record Coastal El Niño events composite pattern of precipitation anomalies.\u003cstrong\u003e f\u003c/strong\u003e Same as (\u003cstrong\u003ee\u003c/strong\u003e) but for Coastal La Niña events. The green boxes indicate the Niño 1+2 and Niño 3.4 regions, from right to left, respectively. The legend represents the number of events in each dataset, while the hatching denotes statistical significance\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7774655/v1/e18685bafb3aee618d95e2d7.png"},{"id":94985723,"identity":"12ce6400-eb39-4866-a7f9-bfcb7875a6f9","added_by":"auto","created_at":"2025-11-03 06:58:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1685383,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eReanalysis data Coastal El Niño events SST and wind regression onto SPMM index. \u003cstrong\u003eb \u003c/strong\u003eSame as (\u003cstrong\u003ea\u003c/strong\u003e) but for NPMM index. \u003cstrong\u003ec\u003c/strong\u003e CESM2-LE historical record Coastal El Niño events SST and wind regression onto SPMM index. \u003cstrong\u003ed\u003c/strong\u003eSame as (\u003cstrong\u003ec\u003c/strong\u003e) but for NPMM index. \u003cstrong\u003ee\u003c/strong\u003e CESM2-LE SSP 3.7 record Coastal El Niño events SST and wind regression onto SPMM index. \u003cstrong\u003ef \u003c/strong\u003eSame as (\u003cstrong\u003ee\u003c/strong\u003e) but for NPMM index. The green boxes indicate the Niño 1+2 and Niño 3.4 regions, from right to left, respectively\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7774655/v1/2b5024dcc629006fd182cd66.png"},{"id":94985385,"identity":"91185afc-827e-4439-ad2e-757da0cc701f","added_by":"auto","created_at":"2025-11-03 06:58:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":938717,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Monthly mean precipitation in the Niño 1+2 region as a function of 3-month running mean season climatology. \u003cstrong\u003eb\u003c/strong\u003e Scatter between 3-month running mean precipitation and 3-month running mean SST in the Niño 1+2 area.\u003cstrong\u003e c\u003c/strong\u003e Same as (\u003cstrong\u003eb\u003c/strong\u003e) but only for February-March-April seasonal average. For historical record regression line (correlation coefficient 0.70) is given approximately by PR = -20 + 0.86(SST) and indicates an increase of approximately 0.9 mm/day for every 1°C increase in SST in the Niño 1+2 region above 23°C. For future record regression line (correlation coefficient 0.84) is given approximately by PR = -26 + 1.08(SST°C) and indicates an increase of approximately 1 mm/day for every 1°C increase in SST in the Niño 1+2 region above 23°C. For observations regression line (correlation coefficient 0.85) in given approximately by PR = -38 + 1.34(SST°C) and indicates an increase of approximately 1.3 mm/day for every 1°C increase in SST in the Niño 1+2 region above 23°C \u003cstrong\u003ed \u003c/strong\u003eSame as (\u003cstrong\u003ec\u003c/strong\u003e) but for anomalies. Shaded areas represent the 10th and 90th percentage respectively\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7774655/v1/bd65fed7d4fc3cc7fe84422a.png"},{"id":94985550,"identity":"a6ff3e0c-9624-4009-9628-570b4eda15f6","added_by":"auto","created_at":"2025-11-03 06:58:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":707784,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003eThirty-one-year running mean of the standard deviation across all CESM2-LE realizations of the February–March–April (FMA) Niño 1+2 and SPMM indices over the period 1945–2085. Shaded envelopes denote the 10th–90th percentile range based on bootstrapped resampling. \u003cstrong\u003eb\u003c/strong\u003e Distribution of the SPMM index in the CESM2-LE during Coastal El Niño (red) and Coastal La Niña (blue) events from 1929 to 2100. Legend specifies the percentage of instances when the SPMM index is above 0 during Coastal El Niño events and bellow 0 during Coastal La Niña events. \u003cstrong\u003ec\u003c/strong\u003e CESM2-LE historical record relationship between SPMM and Niño 1+2 indices in FMA. Red dots denote Coastal El Niño events (warm events) and blue dots denotes Coastal La Niña events (cold events). Each grey dot represents a monthly data point from February to April (FMA) across all years analyzed in the historical record (1929-2014), highlighting the correlation of r = 0.39 between the Niño 1+2 and SPMM indices\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7774655/v1/3241d02953a0596f4e152a05.png"},{"id":94985077,"identity":"11ea5c43-cbb3-4783-8cc8-04d11cbb0547","added_by":"auto","created_at":"2025-11-03 06:57:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":122756,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e CESM2-LE historical coastal events precipitation risk ratios. \u003cstrong\u003eb\u003c/strong\u003e Same as (\u003cstrong\u003ea\u003c/strong\u003e) but for SSP 3.7 coastal events. Shading regions represent 10th and 90th percentiles. Red vertical lines represent the precipitation associated with the 2017 and 2023 events, as label in the graph. The x axis represents precipitation thresholds corresponding to different percentiles, from 5% to 95%\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7774655/v1/c6259ba6a40dd3e2dfac563e.png"},{"id":94990412,"identity":"d706210c-a991-4e68-bf69-7a8c8b8ebb7c","added_by":"auto","created_at":"2025-11-03 07:16:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9341846,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7774655/v1/c252f00d-fd7e-4bbf-8d1b-90b84fbdd42e.pdf"},{"id":94851454,"identity":"aa2f4ba4-99b6-4ccf-b88b-fa1b70dcdf2f","added_by":"auto","created_at":"2025-10-31 11:11:00","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3052106,"visible":true,"origin":"","legend":"","description":"","filename":"20251003SupplementaryMaterialLeandraLoyola.docx","url":"https://assets-eu.researchsquare.com/files/rs-7774655/v1/275c343e6486bb1a6e5e35e0.docx"}],"financialInterests":"","formattedTitle":"Coastal El Niño and La Niña Events in a Changing Climate: Insights from the CESM2 Large Ensemble","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAmong Tropical Pacific Climate phenomena, the El Ni\u0026ntilde;o Southern Oscillation (ENSO) stands out as one of the most significant drivers of global climate variability \u0026nbsp; (Horel \u0026amp; Wallace, 1981; McPhaden et al., 2006). ENSO exerts widespread impacts globally (Jeon et al., 2025), including climate impacts, ecosystem disruptions and fishery production (Cai et al., 2020; Taschetto et al., 2020), making it one of the most extensively studied climate phenomena. Of particular concern is how ENSO will evolve in warmer climate (Cai et al., 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eENSO shows two distinct variants characterized by differing spatial patterns and underlying physical mechanisms and teleconnections. The most extensively studied ENSO variant is characterized by sea surface temperature (SST) anomalies in the far eastern Pacific, known as the Eastern Pacific El Ni\u0026ntilde;o, which can sometimes reach extreme intensity (Takahashi et al., 2011). In contrast, the Central Pacific El Ni\u0026ntilde;o is marked by weaker SST anomalies centered in the central equatorial Pacific. The recognition of these two event types has led to the concept of ENSO diversity (Capotondi et al., 2015). Recent research has focused on determining whether this diversity reflects distinct dynamical regimes or arises from different external forcing conditions (Timmermann et al., 2018). Moreover, ENSO has been linked to an increase in the frequency of marine heatwaves across the Pacific (Gregory et al., 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast to basin-scale ENSO events, a distinct type \u0026mdash;Coastal El Ni\u0026ntilde;o\u0026mdash; has recently emerged. It is characterized by a warming of SST along the coasts of Peru and Ecuador during the late austral summer, while the central equatorial Pacific remains in a cool or near-neutral state (Garreaud, 2018; L\u0026uuml;bbecke et al., 2019; Peng et al., 2024; Takahashi \u0026amp; Mart\u0026iacute;nez, 2019). This warming is driven by a local Bjerknes feedback, in which an initial SST anomaly is amplified through a local atmospheric response involving northerly winds off Ecuador and a southward shift of the Intertropical Convergence Zone (ITCZ) (Takahashi \u0026amp; Mart\u0026iacute;nez, 2019). The initial SST anomaly may be triggered either by intraseasonal oceanic Kelvin wave activity (Echevin et al., 2018; L\u0026uuml;bbecke et al., 2019; Peng et al., 2024) or by remote atmospheric disturbances that affect coastal upwelling (Garreaud, 2018; Martinez-Villalobos et al., 2024; Wei et al., 2025). This type of marine heat wave has gained considerable scientific attention in 2017 following an extreme event that triggered intense rainfall and widespread societal disruptions in Peru and Ecuador that could not be predicted (Yglesias-Gonz\u0026aacute;lez et al., 2023). The 2017 event in Peru resulted in losses of approximately $US 3.100 million (Garreaud, 2018), not including the damage to over 60,000 hectares of agricultural land according to the United Nations (United Nations News, 2017) and the subsequent surge in dengue cases with around 50.000 infections according to the Direcci\u0026oacute;n General de Epidemolog\u0026iacute;a del Per\u0026uacute; (MINSA, 2025) (see also Yglesias-Gonz\u0026aacute;lez et al., (2023)).\u003c/p\u003e\n\u003cp\u003eMore recently, a comparable coastal event occurred in 2023, characterized by even stronger SST anomalies and more intense precipitation (Mart\u0026iacute;nez-Villalobos et al., 2024; Peng et al., 2024; Tan et al., 2024). This latter event further evolved as a basin-scale El Ni\u0026ntilde;o, which was not the case of the 2017 Coastal El Ni\u0026ntilde;o event, which has raised concern whether Coastal El Ni\u0026ntilde;o can influence Tropical Pacific variability (Tan et al., 2024). Moreover, Coastal El Ni\u0026ntilde;o events have been shown to be linked to the Pacific Meridional Modes (PMMs) and to have a cold counterpart, the Coastal La Ni\u0026ntilde;a (Mart\u0026iacute;nez-Villalobos et al., 2024), thus sharing key characteristics with basin-scale El Ni\u0026ntilde;o events. Coastal El Ni\u0026ntilde;o events present a significant challenge for prediction systems due to their rapid evolution and relatively weak persistence in the central Pacific compared to their basin-scale counterpart (Ram\u0026iacute;rez \u0026amp; Briones, 2017; Rivera Tello et al., 2023). They have received limited attention, largely because of sparse coverage in the far eastern Pacific (east of 95\u0026deg;W) and because current generation coupled climate models lack the resolution needed to realistically capture coastal upwelling dynamics. They also exhibit a persistent warm bias in the far eastern Pacific (Geng \u0026amp; Jin, 2023; Luo et al., 2017; Smith et al., 2019) that also may influence Coastal El Ni\u0026ntilde;o dynamics and forcing mechanism. Nevertheless, long-term integrations of climate models offer a valuable resource for their study. Recently Rudloff \u0026amp; L\u0026uuml;bbecke (2025) used the CESM1 Large Ensemble (Kay et al., 2015) to investigate the drivers of Coastal El Ni\u0026ntilde;o events, focusing on features that can make them evolve as basin-scale event. Here we use the next generation of this model resource, the CESM2 Large ensemble (Rodgers et al., 2021), to also characterize Coastal El Ni\u0026ntilde;o drivers focusing on the PMMs. A large ensemble gives us a great opportunity to identify changes on the events internal variability and to assess the model performance (Deser, 2020). Considering their societal impacts, our interest is also to evaluate how CESM2 Large Ensemble projects precipitations during Coastal El Ni\u0026ntilde;o in the future climate. Future increases in local SSTs may in particular enhance the strength of the regional Bjerknes feedback (Fu \u0026amp; Fedorov, 2023), potentially favoring the development of Coastal El Ni\u0026ntilde;o events and associated precipitation. However, this effect could be modulated by a concurrent rise in the convective threshold associated with a more stratified tropical troposphere (Johnson \u0026amp; Xie, 2010). In addition, key drivers of Coastal El Ni\u0026ntilde;o variability\u0026mdash;such as the PMMs and the Madden\u0026ndash;Julian Oscillation (MJO)\u0026mdash;are also expected to exhibit changes under global warming, with implications for the frequency, timing, and spatial expression of these events (Liguori \u0026amp; Di Lorenzo, 2019; Wang et al., 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Given the complex interplay of ocean\u0026ndash;atmosphere interactions governing Coastal El Ni\u0026ntilde;o events, the use of a large ensemble provides a robust framework for assessing future changes in their dynamics. The ensemble\u0026rsquo;s extended temporal coverage and ensemble spread also enable a more rigorous analysis of internal variability (Maher et al., 2023; Rudloff \u0026amp; L\u0026uuml;bbecke, 2025). Our analysis thus provides both insights into the mechanisms of Coastal El Ni\u0026ntilde;o and an assessment of the CESM2 Large Ensemble ability to simulate such events, contributing to the growing body of literature on this unprecedented modeling resource.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The structure of this paper is as follows: Section 2 outlines the method, detailing the datasets used and statistical approach to evaluating the model. Section 3 presents the results, with emphasis on model performance, the relationship between PMMs and coastal events, and the projections of future precipitation associated with these events. Finally, Section 4 provides a discussion of the findings and their broader implications.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Data\u003c/h2\u003e\u003cp\u003eMonthly SST, precipitation and surface wind data from the CESM2 Large Ensemble (hereafter CESM2-LE) (Rodgers et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) are used. Model outputs were interpolated to a regular horizontal grid of 1\u0026deg; by 1\u0026deg;. Two scenarios are used (historical and SSP 3.7) allowing a comparison of two periods of 85 years each: 1929\u0026ndash;2014 and 2015\u0026ndash;2100; 100 members are used resulting in 8500 years of data for each period. To compare model results with observed data we used the NOAA Extended Reconstruction SST v5 reanalysis (Huang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) to obtain monthly SST data at a resolution of 2.5\u0026deg; by 2.5\u0026deg;. Furthermore, we used the Global Precipitation Climatology Project dataset (Adler et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) for monthly precipitation data and the NCEP-NCAR reanalysis 1 (Kalnay et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) for monthly surface wind data, both on a 2.5\u0026deg; by 2.5\u0026deg; grid. For all reanalysis data we used a period from 1980 to 2024.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Indices and Coastal El Ni\u0026ntilde;o classification\u003c/h2\u003e\u003cp\u003ePrior to analysis, we calculated anomalies and 3-month running means across all data. Anomalies in the reanalysis data were computed by subtracting the mean seasonal cycle to the data calculated over the period 1980\u0026ndash;2024, followed by a smoothing process using the first two Fourier harmonics and subsequently linear detrending. For the CESM2-LE, the anomalies were determined by subtracting the time-evolving ensemble mean amongst the 100 available members. Afterward, a 3-month running mean was calculated on the anomalies. The application of the running mean was applied to focus on events during February-March-April (FMA), which is the season in which warming/cooling maximizes the magnitude of associated precipitation anomalies. We classified the events by establishing a threshold for the Ni\u0026ntilde;o 1\u0026thinsp;+\u0026thinsp;2 index (SST anomalies average over 0\u0026deg;-10\u0026deg;S, 90\u0026deg;W-80\u0026deg;W) and Ni\u0026ntilde;o 3.4 index (SST anomalies average over 5\u0026deg;N-5\u0026deg;S,170\u0026deg;W-120\u0026deg;W) (Trenberth, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) during the months of FMA. These months were selected due to their alignment with the rainfall season along the South Pacific coast (Garreaud, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is during this season that minor fluctuations in SST are more likely to result in extreme precipitation (Martinez-Villalobos et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe classify coastal events as follows:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCoastal El Ni\u0026ntilde;o events\u003c/b\u003e: Ni\u0026ntilde;o 1\u0026thinsp;+\u0026thinsp;2 index in FMA\u0026thinsp;\u0026gt;\u0026thinsp;0.3\u0026deg;C and Ni\u0026ntilde;o 3.4 index in FMA \u0026lt; -0.3\u0026deg;C.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCoastal La Ni\u0026ntilde;a events\u003c/b\u003e: Ni\u0026ntilde;o 1\u0026thinsp;+\u0026thinsp;2 index in FMA \u0026lt; -0.3\u0026deg;C and Ni\u0026ntilde;o 3.4 index in FMA \u0026gt;-0.3\u0026deg;C\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eSetting the threshold at 0.3\u0026deg;C strikes a balance between capturing meaningful SST anomalies and including enough events for statistically robust composites.\u003c/p\u003e\u003cp\u003ePMMs indices in this study were computed following the methodology described in Mart\u0026iacute;nez-Villalobos et al. (2024). As in Chiang and Vimont (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), we adjusted the monthly anomalies of SST and wind using the Ni\u0026ntilde;o 3.4 index to remove the basin-wide El Ni\u0026ntilde;o signal. Subsequently, we performed a maximum covariance analysis (MCA) (Bretherton et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) on the adjusted SST and wind data to identify coupled patterns and obtain the South Pacific Meridional Mode (SPMM) (40\u0026deg;S\u0026ndash;0\u0026deg;S, 140\u0026deg;W\u0026ndash;90\u0026deg;W) (Dewitte et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and North Pacific Meridional Mode (NPMM) (21\u0026deg;S\u0026ndash;32\u0026deg;N, 175\u0026deg;E\u0026ndash;95\u0026deg;W) indices. A 3-month running mean was applied to the PMMs indices afterward. To visualize the patterns associated with the indices (PMMs patterns), we regressed in each grid point the SST and wind (with a 3-month running mean) onto the SPMM and NPMM index separately (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInterestingly, the SPMM signal, which is most prominent during the FMA season, is largely captured by the second EOF mode of SST anomalies over the tropical Pacific in the model. The time-series correlation between the SPMM and PC2 indices is r\u0026thinsp;=\u0026thinsp;0.84 for the historical scenario and r\u0026thinsp;=\u0026thinsp;0.71 for the future scenario (Supplementary Fig.\u0026nbsp;3), highlighting the significant influence of the SPMM in the model. Notably, in the large ensemble, PC2 is represented by PC3, due to its closer resemblance to PC2 of the observed data (0.72 spatial correlation coefficient) (Supplementary Figs.\u0026nbsp;5 and 6), suggesting that the SPMM in the model essentially took over the role of PC2, displacing the analogous of PC2 in observations to the third position. As detailed previously, the analysis of PMMs variability required isolating the NPMM from the dominant SPMM signal, which was captured by the first principal component. Consequently, the second principal component was used as a proxy for the NPMM, consistent with the approach outlined in Richter et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (see Supplementary Fig.\u0026nbsp;8).\u003c/p\u003e\u003cp\u003eIn our study, we applied the aforementioned methodology for all datasets, utilizing the more dominant mode of the MCA for both hemispheres, except for the CESM2-LE for which the second mode is used for the NPMM and the first mode for the SPMM. This decision was based on the selection of the pattern that most closely resembled and had the highest correlation to the reanalysis PMM patterns (0.81 and 0.79 correlation coefficient for SPMM and NPMM) (see Supplementary Fig.\u0026nbsp;8).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Model evaluation and statistical analysis\u003c/h2\u003e\u003cp\u003eWe assessed events spatial patterns using composite analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and calculated composite features that are symmetric between Coastal El Ni\u0026ntilde;o and La Ni\u0026ntilde;a events (Supplementary Fig.\u0026nbsp;2) by subtracting the values of the cold phase from the warm phase and dividing the result by two. Conversely, for antisymmetric composite features are determined by adding these values and dividing the sum by two.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe same methodology was used for the model and the reanalysis data.\u003c/p\u003e\u003cp\u003eTo analyze the evolution of coastal events in the CESM2-LE and its relationship with PMMs, we inspect the composite evolutions of the PMMs and Ni\u0026ntilde;o 1\u0026thinsp;+\u0026thinsp;2 indices along with their dispersion diagnosed from the 31-year running mean and standard deviation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe relationship between precipitation and SST is also analyzed as a function of calendar month and amongst events. The climatology for the model was calculated using all simulations, for the historical record (1929\u0026ndash;2014) and the future record (2015\u0026ndash;2100). Climatology for observed data was derived using the span of the record (1980\u0026ndash;2024) (Fig.\u0026nbsp;4). Additionally, we calculated risk ratios to compare historical and future precipitation, which we explain in detail in the next sub-section (Fig.\u0026nbsp;6). Finally, spatial correlations of coastal event composites and symmetric features between CESM2-LE and reanalysis data were computed over the entire tropical Pacific, in the region of 20\u0026deg;S-20\u0026deg;N, 120\u0026deg;E-170\u0026deg;W. The same domain was used to calculate spatial correlations for SST regressions onto Ni\u0026ntilde;o indices.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Risk ratios\u003c/h2\u003e\u003cp\u003eRisk Ratios (RR) are used to estimate changes in probability of occurrence of an event given a certain threshold (Stott et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Martinez-Villalobos \u0026amp; Neelin, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In this study, RRs are used to evaluate the probability of precipitation exceeding various percentiles, comparing future and historical records. We also compared precipitation conditions during coastal events in contrast with normal conditions (months without anomalies associated with coastal and basin-wide El Ni\u0026ntilde;o events).\u003c/p\u003e\u003cp\u003eRR were calculated as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:RR=\\frac{\\raisebox{1ex}{${n}_{1}$}\\!\\left/\\:\\!\\raisebox{-1ex}{${N}_{1}$}\\right.}{\\raisebox{1ex}{${n}_{2}$}\\!\\left/\\:\\!\\raisebox{-1ex}{${N}_{2}$}\\right.}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{2}\\)\u003c/span\u003e\u003c/span\u003e denote the number of events exceeding a given precipitation threshold under historical and future conditions, respectively, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{2}\\)\u003c/span\u003e\u003c/span\u003e are the total number of events for each period. This gives us the ratio of the precipitation probability that Coastal El Ni\u0026ntilde;o events in the future can be associated with relatively to that of an event in the contemporary climate (historical record).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1.\u0026nbsp;Coastal events patterns and model evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this section we evaluate the skill of the CESM2-LE in simulating Coastal El Ni\u0026ntilde;o events. Overall, the model simulates lower frequency of occurrences of warm events relative to cold events in both the historical and future runs, when compared to observational data. In the historical record, Coastal El Ni\u0026ntilde;o events constitute 18% of the total events, significantly lower than the 42% identified (6 of a total of 14 coastal events) in the reanalysis data. Additionally, Coastal El Ni\u0026ntilde;o events decrease their frequency by 40% in the model future scenario, while Coastal La Ni\u0026ntilde;a events do not have a significant change in their frequency. In terms of spatial patterns, Coastal El Ni\u0026ntilde;o events in the CESM2-LE historical simulations exhibit positive SST anomalies that extend both centrally and southward, extending as far as central Chile and reaching beyond the Ni\u0026ntilde;o 3.4 region (Fig. 1c). This westward extension has been previously observed in CMIP5 models, when simulating basin-wide El Ni\u0026ntilde;o events (Luo et al., 2017). Compared to observational data (Fig. 1a), the model simulates a more pronounced southern extension, with a spatial correlation between composite maps only reaching 0.50 correlation coefficient in the 20\u0026deg;S-20\u0026deg;N, 160\u0026deg;E-70\u0026deg;W band. Similarly, the composite pattern for Coastal La Ni\u0026ntilde;a events in the CESM2- LE historical run shows negative SST anomalies extending southward and westward from the Ni\u0026ntilde;o 1+2 region (Fig. 1d) more in the model than in the observations. The composite SST anomaly pattern correlation reaches 0.70 in the 20\u0026deg;S-20\u0026deg;N, 160\u0026deg;E-70\u0026deg;W band.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough the model tends to simulate spatially broader SST anomaly patterns than those observed, it successfully captures the main characteristics of coastal warming events. Its performance is comparable to that of the previous CESM version (CESM1) (Karamperidou \u0026amp; DiNezio, 2022; Rudloff \u0026amp; L\u0026uuml;bbecke, 2025). However, coastal events in CESM1 typically exhibit a temporal lag, with SST anomalies peaking during April\u0026ndash;June (AMJ), as opposed to the February\u0026ndash;April (FMA) peak period in the observed data (Rudloff \u0026amp; L\u0026uuml;bbecke, 2025b). The CESM2-LE does present this lag but not in the majority of events, specifically, the peak of Coastal El Ni\u0026ntilde;o events distributes from February to June (Supplementary Fig. 9).\u003c/p\u003e\n\u003cp\u003eWind anomalies associated with coastal events are also generally well represented in the CESM2-LE simulations. Northerly wind anomalies along the equator and a weakening of the easterly trades frequently accompanied by westerly wind anomalies observed during warm Coastal El Ni\u0026ntilde;o events (Rodr\u0026iacute;guez-Morata et al., 2019), are present in the simulated Coastal El Ni\u0026ntilde;o events in the historical scenario (Fig. 1c). On the other hand, Coastal La Ni\u0026ntilde;a events in the model exhibit anomalous wind patterns marked by a predominantly northward and westward flow (Fig. 1c), consistent with the expected atmospheric circulation associated with cold anomalies along the southeastern Pacific coast (Martinez-Villalobos et al., 2024).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The composite precipitation anomaly pattern simulated by CESM2-LE exhibits notable similarity to observations, with a pronounced precipitation maximum centered over the Ni\u0026ntilde;o 1+2 region (Fig. 2d). Nevertheless, the model reproduces an eastward extension of the anomaly field, compared to observations, reaching approximately 145\u0026deg;W. The spatial correlation between modeled and observed precipitation composites within the 20\u0026deg;S\u0026ndash;20\u0026deg;N,\u0026nbsp;160\u0026deg;E-70\u0026deg;W band is r = 0.51. For Coastal La Ni\u0026ntilde;a events precipitation pattern, the model demonstrates comparable skill, with a spatial correlation of 0.43.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Additionally, the model\u0026rsquo;s precipitation climatology time-series for the FMA season shows a high correlation with observations (r = 0.98), albeit with a positive bias of approximately 1 mm/day (Fig. 4a). Notably, although the model simulates higher precipitation totals during FMA, the sensitivity of precipitation to SST anomalies is greater in observations. Specifically, precipitation increases by 0.9 mm/day per 1\u0026deg;C SST increase in the model, while observed data indicate a stronger response of 1.4 mm/day per 1\u0026deg;C (Fig. 4c). This indicates that while the model captures the large-scale relationship between SST and rainfall, it underestimates the strength of the observed precipitation response in the Ni\u0026ntilde;o 1+2 region.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;When considering all calendar months, CESM2-LE accurately captures the overall relationship between SST and precipitation anomalies (Fig. 4b). However, for Coastal La Ni\u0026ntilde;a events, the model exhibits a marked positive precipitation bias, characterized by substantially higher precipitation values (Fig. 4c) despite simulating SST anomalies of similar magnitude to those observed (Fig. 4d). In contrast, Coastal El Ni\u0026ntilde;o events show good agreement with observations in terms of both absolute precipitation and SST anomaly amplitudes (Figs. 4c and 4d). The excessive rainfall during La Ni\u0026ntilde;a events suggests a positive bias in the model\u0026rsquo;s precipitation response to SST cooling. To further investigate the structural features contributing to these biases, we decompose the composite SST anomalies into their symmetric and anti-symmetric components. This decomposition reveals that the model replicates the large-scale structure of both components with moderate skill, yielding spatial correlation coefficients of r=0.57 for symmetric and r=0.60 for antisymmetric features (Supplementary Figs. 2a and 2b) within the 20\u0026deg;S\u0026ndash;20\u0026deg;N, 160\u0026deg;E-70\u0026deg;W band. These results indicate that while CESM2-LE broadly captures the spatial organization\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eof coastal SST anomalies, subtle asymmetries and pattern extensions\u0026mdash;particularly during La Ni\u0026ntilde;a phases\u0026mdash;may contribute to the discrepancy between model and observation.\u0026nbsp;Symmetric characteristics are\u003c/p\u003e\n\u003cp\u003epredominantly concentrated in the Ni\u0026ntilde;o 1+2 region and exhibit a southward extension toward the Ni\u0026ntilde;o 3.4 region in both the historical and future simulations (Supplementary Figs. 2d and 2f). While this general pattern is also evident in the observations, the spatial extent in the observed data does not reach into the Ni\u0026ntilde;o 3.4 region. This indicates that the model successfully captures part of the characteristics associated with the dynamics of both coastal warm and cold phases, with a more pronounced representation of antisymmetric dynamics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe antisymmetric features are predominantly concentrated in the Ni\u0026ntilde;o 1+2 and Ni\u0026ntilde;o 3.4 regions, including the dipole that extends from the north-central Pacific to the southern Ni\u0026ntilde;o 1+2 region. This pattern is more clearly defined in the model simulations, where it becomes apparent that coastal warm and cold events exhibit distinct spatial structures, with no evident pattern similarity in this region. In contrast to the observed data, the CESM2-LE demonstrates a strong correlation between the antisymmetric and symmetric features with PMMs. The symmetric features in the historical record exhibit a spatial correlation coefficient of 0.81, with their corresponding SPMM pattern within the 40\u0026deg;S\u0026ndash;0\u0026deg;N, 160\u0026deg;E-70\u0026deg;W band. Conversely, the NPMM shows an inverse correlation with the antisymmetric features, with a correlation coefficient of -0.37. This inverse relationship is not observed in observation data.\u003c/p\u003e\n\u003cp\u003eThese similarities imply that, within the CESM2-LE, the SPMM may play a key role in modulating the spatial pattern and temporal evolution of Coastal El Ni\u0026ntilde;o events, whereas the NPMM appears to govern processes that distinguish warm from cold phases. However, it is important to note that SPMM and NPMM exhibit reduced statistical independence in CESM2-LE compared to observations, often manifesting as opposing patterns, which may limit their distinct interpretability in the model.\u003c/p\u003e\n\u003cp\u003eFinally, in terms of the Ni\u0026ntilde;o indices, the model represents correctly the spatial regressions of the Ni\u0026ntilde;o 1+2 and Ni\u0026ntilde;o 3.4 indices compared with observed data (Supplementary Fig. 1), although we can note a bias in SST anomalies with a westward extension of the pattern. This increased intensity of the model representation of the indices can be also observed in the relationship between the indices with a correlation between the Ni\u0026ntilde;o 1+2 index and the Ni\u0026ntilde;o 3.4 index of r=0.87, compared to a r=0.64 correlation coefficient in observations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.\u0026nbsp;Relationship with Pacific Meridional Modes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs outlined in the introduction, PMMs have recently been implicated in both the enhancement and initiation of Coastal El Ni\u0026ntilde;o events (Mart\u0026iacute;nez-Villalobos et al., 2024). Accordingly, evaluating the representation of PMMs within the model is essential for interpreting the spatial expression and projected evolution of coastal climate variability.\u003c/p\u003e\n\u003cp\u003eTo evaluate the influence of PMMs on coastal variability, we first examine the relationship between the PMMs indices and the Ni\u0026ntilde;o 1+2 index. As illustrated in Supplementary Figure 4, the NPMM and Ni\u0026ntilde;o 1+2 indices during FMA are in fact independent (r=0.08 and -0.09 for the historical and SSP 3.7 scenario respectively), whereas in observations, they are somewhat linearly related to r=0.39 (statistically significant at the 95% level). In contrast, the SPMM displays a similar level of association with the Ni\u0026ntilde;o 1+2 index across both model simulations and observational records (Supplementary Figs. 4a and 4b) (r=0.39 and r=0.37, for the model historical record and observations, respectively), suggesting a more systematic linkage.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnalysis of composite Coastal El Ni\u0026ntilde;o events reveals however a notable correspondence between modeled SST anomalies and PMMs patterns. In the CESM2-LE historical simulation, the spatial correlation between coastal warm events composites and the SPMM reaches 0.73 within the 40\u0026deg;S\u0026ndash;0\u0026deg;N, 160\u0026deg;E-70\u0026deg;W band (Fig. 3c), increasing substantially to 0.86 under future forcing conditions (Fig. 3e). By comparison, the observed spatial correlation is lower, at 0.32 (Fig. 3a). This alignment is reflected in the model\u0026rsquo;s pronounced extension of coastal warming anomalies eastward and southward from the Ni\u0026ntilde;o 1+2 region (Figs. 1c and 1e), closely resembling the SPMM structure depicted in Figures 3c and 3e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCoastal La Ni\u0026ntilde;a events (Figs. 1.d and 1.f) show similar behavior. They exhibit strong inverse correlations with the SPMM pattern, (r=-0.75 historical and r=-0.78 future, within the 40\u0026deg;S\u0026ndash;0\u0026deg;N, 160\u0026deg;E-70\u0026deg;W band), reflecting similar southward extensions as in observed warm events. This contrasts with the spatial correlation of r=-0.07 between observed cold coastal events (Fig. 1b) and SPMM.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese findings highlight a notable bias in the PMMs influence structure within the model. Unlike the reanalysis data, where NPMM is more dominant than tSPMM (e.g., Martinez-Villalobos et al., 2024), the model exhibits a particularly strong SPMM signal in coastal events, while NPMM has less influence on coastal events than its observed counterpart.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis PMMs pattern bias is also reflected in precipitation. We observed a composite of precipitation anomalies when the SPMM index is above 1, that strongly resembles the precipitation composite of Coastal El Ni\u0026ntilde;o events, likewise for Coastal La Ni\u0026ntilde;a events but with opposite sign for the SPMM index (see Supplementary Figures 7c and 7e). This highlights that the influence of the SPMM in coastal events in the model does not just limit to their SST anomalies, but it is involved in the intensity of precipitation too.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. Coastal Events Precipitation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrecipitation is the most consequential aspect of Coastal El Ni\u0026ntilde;o events and thus the central variable examined in this study. As demonstrated earlier, precipitation is accurately captured by the CESM2-LE, exhibiting low bias and correctly simulated patterns, along with precipitation linked to the SPMM. We now examine how precipitation associated with coastal events is projected to evolve under future climate conditions.\u003c/p\u003e\n\u003cp\u003eProjections indicate a general increase in precipitation of approximately 2 mm day⁻\u0026sup1; during the FMA period under future climate conditions (Fig. 4a), with localized maxima reaching up to 5 mm day⁻\u0026sup1; in association with elevated SSTs approaching 34 \u0026deg;C (Fig. 4b). In the context of future coastal events, 18% of Coastal La Ni\u0026ntilde;a episodes are characterized by precipitation anomalies below \u0026ndash;2 mm day⁻\u0026sup1; (Fig. 4d), in stark contrast to only 0.3% of such events in the model historical record (Fig. 4c).\u003c/p\u003e\n\u003cp\u003eThese findings point to a marked intensification of precipitation deficits associated with cold-phase coastal events under future warming conditions. However, as shown in Figure 4c, the absolute precipitation values during future Coastal La Ni\u0026ntilde;a events are projected to exceed those in the model\u0026rsquo;s historical baseline. This implies that, despite the enhanced intensity of cold events SST anomalies, the increase in mean-state precipitation under warming scenarios could result in Coastal La Ni\u0026ntilde;a episodes that produce more rainfall than their historical counterparts.\u003c/p\u003e\n\u003cp\u003eAlthough peak precipitation anomalies between the historical and future records show no substantial differences, elevated future SST leads to an overall increase in FMA precipitation during both Coastal El Ni\u0026ntilde;o and Coastal La Ni\u0026ntilde;a events. A significant proportion of future events will exceed the precipitation levels observed during the 2017 (Echevin et al., 2018) and 2023 (Hu et al., 2019) events\u0026mdash;approximately 4 mm/day (Fig. 6c)\u0026mdash;with 38.7% of total events surpassing this threshold, including some Coastal La Ni\u0026ntilde;a events, despite stronger cold anomalies projected for cold events in future projections (Fig. 4d).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor Coastal El Ni\u0026ntilde;o events, although SST and precipitation anomalies remain within the same range as in the historical period, 51% of future warm events are projected to reach precipitation levels comparable to those recorded during the extreme basin-wide El Ni\u0026ntilde;o events of 1982\u0026ndash;83 and 1997\u0026ndash;98 \u0026nbsp;(Martinez-Villalobos et al., 2024), with precipitation exceeding 7 mm/day.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis is further quantified in Fig. 6, which shows through a risk ratio that the probability of future Coastal El Ni\u0026ntilde;o events exceeding the 2017 and 2023 events precipitation is approximately 10 times higher than in the historical record. For Coastal La Ni\u0026ntilde;a events, the risk ratio rises to nearly 100 for future events to precipitate more than their historical counterparts (Fig. 6b). This is consistent with Fig. 6c and the finding that the risk of future Coastal La Ni\u0026ntilde;a events producing below-normal precipitation is lower than that of historical cold events doing so.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summary, coastal event precipitation in CESM2-LE appears increasingly driven by externally forced changes in the mean climate state. While internal variability remains important, the dominant signal in future scenarios is one of enhanced precipitation \u0026mdash;even during cold events\u0026mdash; driven by elevated SST. This could reduce the likelihood of truly dry Coastal La Ni\u0026ntilde;a periods and reshape how such events affect regional impacts.\u003c/p\u003e"},{"header":"4. Discussion and concluding remarks","content":"\u003cp\u003eUnderstanding and projecting the behavior of Coastal El Ni\u0026ntilde;o events remains a major challenge in climate science. Their rapid onset, short persistence, and sensitivity to regional processes distinguish them from basin-wide events, complicating both prediction and model evaluation. In this study, we assessed how CESM2-LE simulates the characteristics and projected changes of Coastal El Ni\u0026ntilde;o and La Ni\u0026ntilde;a events under a high-emissions scenario, with a particular focus on SST patterns, PMMs dynamics, and precipitation impacts. From this analysis we can highlight three main findings:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe overly active SPMM in the CESM2-LE and its influence in the performance of the model in simulating Coastal events.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe decrease in frequency of Coastal El Ni\u0026ntilde;o events but clear increase in their intensity in terms of precipitation.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe potential role of future Coastal La Ni\u0026ntilde;a events in providing temporary windows of moderated SST and precipitation anomalies under otherwise extreme conditions driven by anthropogenic warming.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.1. SPMM in the CESM2-LE and its role for Coastal El Ni\u0026ntilde;o events.\u003c/h2\u003e\u003cp\u003eIn terms of the evaluation of the model in simulating Coastal El Ni\u0026ntilde;o events, we found important structural biases in the CESM2-LE. The model reproduces the basic SST-precipitation relationships and large-scale PMMs patterns reasonably well, but it overemphasizes the SPMM and underrepresents the NPMM, especially during the FMA season. This imbalance affects both the spatial structure of coastal events and their classification in principal component space, with PC2 in the model dominated by the SPMM. Although CESM2-LE captures the observed correlation between Ni\u0026ntilde;o 1\u0026thinsp;+\u0026thinsp;2 SST and SPMM variability, it fails to reproduce the strength of the NPMM connection. These biases may limit the realism of the dynamics that shape event diversity and evolution.\u003c/p\u003e\u003cp\u003eTo further understand this structural problem, we investigate the long-term covariability between PMMs and coastal events Ni\u0026ntilde;o 1\u0026thinsp;+\u0026thinsp;2 index values to assess their potential role in modulating multidecadal changes. Notably, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003ea highlights a strong correlation (r\u0026thinsp;=\u0026thinsp;0.88) between the 31-year running mean standard deviation of the SPMM and the Ni\u0026ntilde;o 1\u0026thinsp;+\u0026thinsp;2 index, indicating a tight coupling between low-frequency SPMM variability and Ni\u0026ntilde;o 1\u0026thinsp;+\u0026thinsp;2 region activity. This suggests that variability in the Ni\u0026ntilde;o 1\u0026thinsp;+\u0026thinsp;2 region during FMA is linked to the variability of the SPMM during the same period.\u003c/p\u003e\u003cp\u003eFor coastal events, we find that Coastal El Ni\u0026ntilde;o episodes are consistently associated with positive values of the SPMM index, while Coastal La Ni\u0026ntilde;a events correspond to negative SPMM index values throughout the 1929\u0026ndash;2100 period (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). We observe a significant 86% of Coastal El Ni\u0026ntilde;o events associated with positive SPMM instances and 77% of Coastal La Ni\u0026ntilde;a events associated with negative SPMM instances, highlighting a possible role of the SPMM as precursor and enhancer of coastal events. Additionally, this symmetry reinforces the hypothesis that SPMM variability influences the occurrence of both warm and cold coastal events.\u003c/p\u003e\u003cp\u003eThe concurrent variability in both amplitude and frequency of SPMM and Coastal El Ni\u0026ntilde;o/La Ni\u0026ntilde;a events over the 1929\u0026ndash;2100 period suggests a robust, low-frequency coupling between subtropical and eastern Pacific variability. The systematic alignment in sign, variability strength, and event occurrence across decades supports the hypothesis that SPMM dynamics play a central role in modulating the occurrence and characteristics of coastal events in the CESM2-LE. This relationship, while not perfectly linear or phase-synchronized, implies a common background modulation or a nonlinear interaction pathway that may enhance predictability of coastal climate anomalies on decadal timescales.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Coastal Events in the future\u003c/h2\u003e\u003cp\u003eIn contrast to basin-wide El Ni\u0026ntilde;o events, the projection of coastal events poses a significant challenge for climate models, as evidenced by the performance of CESM2-LE in this study. Nonetheless, the model outputs yield valuable insights into the dynamics and predictability of such events.\u003c/p\u003e\u003cp\u003eThe model projects a substantial decline in the frequency of Coastal El Ni\u0026ntilde;o events during the 21st century, with an estimated reduction of approximately 40% relative to the previous century. On the other hand, Coastal La Ni\u0026ntilde;a events do not exhibit a significant change between the present and future climates, although they represent more than 80% of total events. Despite this, our analysis reveals a robust increase in precipitation associated with coastal events in the future, driven primarily by warmer SSTs. Notably, 52% of future warm events exceed the maximum historical precipitation threshold of 6.8mm/day. Even cold events, historically associated with suppressed rainfall (e.g., Martinez-Villalobos et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), show enhanced precipitation \u0026mdash;suggesting that future Coastal La Ni\u0026ntilde;a events may resemble historically neutral or even wet conditions. This intensification is quantified using risk ratios (Fig.\u0026nbsp;6), which show that the probability of extreme precipitation in future Coastal La Ni\u0026ntilde;a events is up to 100 times greater than in Coastal La Ni\u0026ntilde;a events during the historical record. Such findings suggest that externally forced climate change, rather than internal variability, is becoming the dominant driver of coastal event impacts.\u003c/p\u003e\u003cp\u003eEven considering the projected intensification of mean-state SST and precipitation conditions in the Ni\u0026ntilde;o 1\u0026thinsp;+\u0026thinsp;2 region by the third decade of the 21st century, Coastal El Ni\u0026ntilde;o events will continue to pose a significant hazard. Model projections indicate that these events will produce precipitation rates exceeding climatological averages in the future, with magnitudes surpassing those observed in the recent events of 2017 and 2023. As illustrated in Fig.\u0026nbsp;6b, the associated risk ratio suggests an increased likelihood of Coastal El Ni\u0026ntilde;o events yielding above-average rainfall, thereby amplifying the severity of their impacts relative to their historical counterparts.\u003c/p\u003e\u003cp\u003eDespite the structural biases in PMMs representation, notably an overly energetic SPMM and an underrepresented NPMM, CESM2-LE captures key features relevant to coastal events impacts. Its precipitation response to SPMM forcing closely matches observational patterns (Supplementary Fig.\u0026nbsp;7), and the model reproduces the strong linkage between coastal SST anomalies and rainfall in the Ni\u0026ntilde;o 1\u0026thinsp;+\u0026thinsp;2 region. These strengths suggest that, even with limitations in the simulation of the underlying modes, CESM2-LE provides valuable insights into how Coastal El Ni\u0026ntilde;o and La Ni\u0026ntilde;a events may evolve in a warming climate.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Coastal La Ni\u0026ntilde;a as Climatic Interludes Between Extreme Phases\u003c/h2\u003e\u003cp\u003eWhile the impacts of Coastal El Ni\u0026ntilde;o events have received considerable attention in the literature, Coastal La Ni\u0026ntilde;a events remain comparatively understudied. In this work, we show that CESM2-LE projects no statistically significant changes in future coastal cold events. As illustrated in Fig.\u0026nbsp;4c, although future Coastal La Ni\u0026ntilde;a events are projected to display stronger cold SST anomalies, the concurrent increase in mean-state precipitation and SST reduces their relative intensity compared to historical counterparts. Thus, while rainfall anomalies may become more negative, the higher baseline means absolute precipitation often exceeds historical Coastal La Ni\u0026ntilde;a totals. In fact, these future events are expected to be associated with higher SST and rainfall than those observed during historical cold episodes, so their roles as \u0026ldquo;windows of relief\u0026rdquo; should be understood relative to even wetter future El Ni\u0026ntilde;o conditions, not relative to the past.\u003c/p\u003e\u003cp\u003eThis shift presents a potential opportunity; under a warming and wetter climate regime, Coastal La Ni\u0026ntilde;a events may offer transient periods of reduced precipitation extremes. As shown in Fig.\u0026nbsp;6b, the likelihood that these events will produce below-normal rainfall in the future scenario, creating windows of relative atmospheric and oceanic stability. Notably, 64% of all FMA periods in the future scenario are projected to exceed the rainfall observed during the 2017 event, with precipitation exceeding 7 mm/day, underscoring the importance of the role that Coastal La Ni\u0026ntilde;a events might have in the future.\u003c/p\u003e\u003cp\u003eCoastal El Ni\u0026ntilde;o events have disproportionate impacts on society, particularly in Peru and Ecuador, where fisheries, infrastructure, and public health are highly vulnerable to changes in rainfall (Yglesias-Gonz\u0026aacute;lez et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The 2017 and 2023 events, though driven by different precursors (Hu et al., 2019; Peng et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), each caused widespread damage, highlighting the diverse origins of these events and the limits of current predictive systems. As this study shows, even cold events may no longer provide relief from heavy rainfall, and that preparedness efforts should account for a broader range of extreme outcomes.\u003c/p\u003e\u003cp\u003eIn summary, CESM2-LE successfully captures the fundamental SST\u0026ndash;precipitation relationship associated with coastal events, yet exhibits structural biases\u0026mdash;namely, an overly energetic SPMM and a dampened NPMM \u0026mdash;that influence the spatial patterns and variability of these events. Under the SSP 3.7 scenario, projections indicate that mean-state warming exerts a dominant influence: warm coastal events become less frequent (40% decline), while FMA precipitation increases by approximately 2 mm day⁻\u0026sup1;, and the probability of extreme rainfall rises markedly across both warm and cold phases. Future warm events frequently reach precipitation levels comparable to historical extremes, whereas cold events unfold within a wetter climatological background, often resembling present-day neutral-to-wet conditions. This suggests a diminished likelihood of truly dry Coastal La Ni\u0026ntilde;a episodes. These findings underscore the central role of mean-state changes in shaping future impacts along the Peru\u0026ndash;Ecuador coast and highlight the importance of improving PMMs representation and near-coastal process dynamics in climate models to enhance predictive skill and risk management.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNOAA Extended Reconstruction SST v5 reanalysis can be accessed at https://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.html. The Global Precipitation Climatology Project dataset can be accessed at https://psl.noaa.gov/data/gridded/data.gpcp.html. The NCEP-NCAR reanalysis v1 can be accessed at https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html. Data from the CESM2 Large Ensemble can be accessed at https://www.cesm.ucar.edu/community-projects/lens2/data-sets. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll codes used to perform the corresponding analysis are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the CESM2 Large Ensemble Community Project and supercomputing resources provided by the IBS Center for Climate Physics in South Korea. BD acknowledges supports from ANID (Concurso de Fortalecimiento al Desarrollo Científico de Centros Regionales 2020-R20F0008-CEAZA, COPAS COASTAL FB210021 and Fondecyt Regular 1231174). C.M.-V. acknowledges support from Proyecto ANID Fondecyt Regular, Proyecto ANID Iniciación 11250471 and Data Observatory Foundation ANID Technology Center number DO210001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Data collection and analyses was performed by Leandra Loyola, with assistance by Cristian Martínez-Villalobos and Boris Dewitte. Leandra Loyola wrote the first draft, and all authors contributed with consecutive versions of the manuscript. All authors read and approved the final manuscript. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., \u0026amp; Nelkin, E. (2003). The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979\u0026ndash;Present). Journal of Hydrometeorology, 4(6), 1147-1167. https://doi.org/10.1175/1525-7541(2003)004\u0026lt;1147:TVGPCP\u0026gt;2.0.CO;2\u003c/li\u003e\n\u003cli\u003eBretherton, C. S., Smith, C., \u0026amp; Wallace, J. M. (1992). An Intercomparison of Methods for Finding Coupled Patterns in Climate Data. Journal of Climate, 5(6), 541-560. https://doi.org/10.1175/1520-0442(1992)005\u0026lt;0541:AIOMFF\u0026gt;2.0.CO;2\u003c/li\u003e\n\u003cli\u003eCai, W., McPhaden, M. J., Grimm, A. M., Rodrigues, R. R., Taschetto, A. S., Garreaud, R. D., Dewitte, B., Poveda, G., Ham, Y., Santoso, A., Ng, B., Anderson, W., Wang, G., Geng, T., Jo, H., Marengo, J. A., Alves, L. M., Osman, M., Li, S., . . . Vera, C. (2020). Climate impacts of the El Ni\u0026ntilde;o\u0026ndash;Southern Oscillation on South America. Nature Reviews Earth \u0026amp; Environment, 1(4), 215-231. https://doi.org/10.1038/s43017-020-0040-3\u003c/li\u003e\n\u003cli\u003eCai, W., Santoso, A., Collins, M., Dewitte, B., Karamperidou, C., Kug, J., Lengaigne, M., McPhaden, M. J., Stuecker, M. F., Taschetto, A. S., Timmermann, A., Wu, L., Yeh, S., Wang, G., Ng, B., Jia, F., Yang, Y., Ying, J., Zheng, X., . . . Zhong, W. (2021). Changing El Ni\u0026ntilde;o\u0026ndash;Southern Oscillation in a warming climate. Nature Reviews Earth \u0026amp; Environment, 2(9), 628-644. https://doi.org/10.1038/s43017-021-00199-z\u003c/li\u003e\n\u003cli\u003eCapotondi, A., Wittenberg, A. T., Newman, M., Di Lorenzo, E., Yu, J., Braconnot, P., Cole, J., Dewitte, B., Giese, B., Guilyardi, E., Jin, F., Karnauskas, K., Kirtman, B., Lee, T., Schneider, N., Xue, Y., \u0026amp; Yeh, S. (2014). Understanding ENSO diversity. Bulletin Of The American Meteorological Society, 96(6), 921-938. https://doi.org/10.1175/bams-d-13-00117.1\u003c/li\u003e\n\u003cli\u003eChiang, J. C. H., \u0026amp; Vimont, D. J. (2004). Analogous Pacific and Atlantic Meridional Modes of Tropical Atmosphere\u0026ndash;Ocean Variability*. Journal Of Climate, 17(21), 4143-4158. https://doi.org/10.1175/jcli4953.1\u003c/li\u003e\n\u003cli\u003eDeser, C. (2020). \u0026ldquo;Certain Uncertainty: The Role of Internal Climate Variability in Projections of Regional Climate Change and Risk Management\u0026rdquo;. Earth S Future, 8(12). https://doi.org/10.1029/2020ef001854\u003c/li\u003e\n\u003cli\u003eDewitte, B., Concha, E., Saavedra, D., Pizarro, O., Martinez-Villalobos, C., Gushchina, D., Ramos, M., \u0026amp; Montecinos, A. (2023). The ENSO-induced South Pacific Meridional mode. Frontiers In Climate, 4. https://doi.org/10.3389/fclim.2022.1080978\u003c/li\u003e\n\u003cli\u003eEchevin, V., Colas, F., Espinoza-Morriberon, D., Vasquez, L., Anculle, T., \u0026amp; Gutierrez, D. (2018). Forcings and Evolution of the 2017 Coastal El Ni\u0026ntilde;o Off Northern Peru and Ecuador. Frontiers In Marine Science, 5. https://doi.org/10.3389/fmars.2018.00367\u003c/li\u003e\n\u003cli\u003eFu, M., \u0026amp; Fedorov, A. (2023). The Role of Bjerknes and Shortwave Feedbacks in the Tropical Pacific SST Response to Global Warming. Geophysical Research Letters, 50(19). https://doi.org/10.1029/2023gl105061\u003c/li\u003e\n\u003cli\u003eGarreaud, R. D. (2018). A plausible atmospheric trigger for the 2017 coastal El Ni\u0026ntilde;o. International Journal Of Climatology, 38(S1). https://doi.org/10.1002/joc.5426\u003c/li\u003e\n\u003cli\u003eGeng, T., Jia, F., Cai, W., Wu, L., Gan, B., Jing, Z., Li, S., \u0026amp; McPhaden, M. J. (2023). Increased occurrences of consecutive La Ni\u0026ntilde;a events under global warming. Nature, 619(7971), 774-781. https://doi.org/10.1038/s41586-023-06236-9\u003c/li\u003e\n\u003cli\u003eGregory, C. H., Artana, C., Lama, S., Le\u0026oacute;n‐FonFay, D., Sala, J., Xiao, F., Xu, T., Capotondi, A., Martinez‐Villalobos, C., \u0026amp; Holbrook, N. J. (2024). Global Marine Heatwaves Under Different Flavors of ENSO. Geophysical Research Letters, 51(20). https://doi.org/10.1029/2024gl110399\u003c/li\u003e\n\u003cli\u003eHorel, J. D., \u0026amp; Wallace, J. M. (1981). Planetary-Scale Atmospheric Phenomena Associated with the Southern Oscillation. Monthly Weather Review, 109(4), 813-829. https://doi.org/10.1175/1520-0493(1981)109\u0026lt;0813:PSAPAW\u0026gt;2.0.CO;2\u003c/li\u003e\n\u003cli\u003eHuang, B., Thorne, P. W., Banzon, V. F., Boyer, T., Chepurin, G., Lawrimore, J. H., Menne, M. J., Smith, T. M., Vose, R. S., \u0026amp; Zhang, H. (2017). Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal Of Climate, 30(20), 8179-8205. https://doi.org/10.1175/jcli-d-16-0836.1\u003c/li\u003e\n\u003cli\u003eHu, Z., Huang, B., Zhu, J., Kumar, A., \u0026amp; McPhaden, M. J. (2018). On the variety of coastal El Ni\u0026ntilde;o events. Climate Dynamics, 52(12), 7537-7552. https://doi.org/10.1007/s00382-018-4290-4\u003c/li\u003e\n\u003cli\u003eJeon, Y., Kim, S., Sohn, S., Han, S., \u0026amp; Moon, S. (2025). Toward a Sustainable and Resilient Society through Enhanced ENSO Response and Preparedness. Bulletin Of The American Meteorological Society, 106(4), E571-E575. https://doi.org/10.1175/bams-d-25-0024.1\u003c/li\u003e\n\u003cli\u003eJohnson, N. C., \u0026amp; Xie, S. (2010). Changes in the sea surface temperature threshold for tropical convection. Nature Geoscience, 3(12), 842-845. https://doi.org/10.1038/ngeo1008\u003c/li\u003e\n\u003cli\u003eKalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., \u0026amp; Joseph, D. (1996). The NCEP/NCAR 40-Year Reanalysis Project. Bulletin of the American Meteorological Society, 77(3), 437-472. https://doi.org/10.1175/1520-0477(1996)077\u0026lt;0437:TNYRP\u0026gt;2.0.CO;2\u003c/li\u003e\n\u003cli\u003eKay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G., Arblaster, J. M., Bates, S. C., Danabasoglu, G., Edwards, J., Holland, M., Kushner, P., Lamarque, J., Lawrence, D., Lindsay, K., Middleton, A., Munoz, E., Neale, R., Oleson, K., . . . Vertenstein, M. (2014). The Community Earth System Model (CESM) Large Ensemble Project: A Community Resource for Studying Climate Change in the Presence of Internal Climate Variability. Bulletin Of The American Meteorological Society, 96(8), 1333-1349. https://doi.org/10.1175/bams-d-13-00255.1\u003c/li\u003e\n\u003cli\u003eKaramperidou, C., \u0026amp; DiNezio, P. N. (2022). Holocene hydroclimatic variability in the tropical Pacific explained by changing ENSO diversity. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-34880-8\u003c/li\u003e\n\u003cli\u003eLiguori, G., \u0026amp; Di Lorenzo, E. (2019). Separating the North and South Pacific Meridional Modes Contributions to ENSO and Tropical Decadal Variability. Geophysical Research Letters, 46(2), 906-915. https://doi.org/10.1029/2018gl080320\u003c/li\u003e\n\u003cli\u003eL\u0026uuml;bbecke, J. F., Rudloff, D., \u0026amp; Stramma, L. (2019). Stand‐Alone Eastern Pacific Coastal warming events. Geophysical Research Letters, 46(21), 12360-12367. https://doi.org/10.1029/2019gl084479\u003c/li\u003e\n\u003cli\u003eLuo, J., Liu, G., Hendon, H., Alves, O., \u0026amp; Yamagata, T. (2017). Inter-basin sources for two-year predictability of the multi-year La Ni\u0026ntilde;a event in 2010\u0026ndash;2012. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-01479-9\u003c/li\u003e\n\u003cli\u003eMaher, N., Wills, R. J., DiNezio, P., Klavans, J., Milinski, S., Sanchez, S. C., Stevenson, S., Stuecker, M. F., \u0026amp; Wu, X. (2023). The future of the El Ni\u0026ntilde;o\u0026ndash;Southern Oscillation: using large ensembles to illuminate time-varying responses and inter-model differences. Earth System Dynamics, 14(2), 413-431. https://doi.org/10.5194/esd-14-413-2023\u003c/li\u003e\n\u003cli\u003eMartinez-Villalobos, C., Dewitte, B., Garreaud, R. D., \u0026amp; Loyola, L. (2024). Extreme coastal El Ni\u0026ntilde;o events are tightly linked to the development of the Pacific Meridional Modes. Npj Climate And Atmospheric Science, 7(1). https://doi.org/10.1038/s41612-024-00675-5\u003c/li\u003e\n\u003cli\u003eMartinez‐Villalobos, C., \u0026amp; Neelin, J. D. (2018). Shifts in Precipitation Accumulation Extremes During the Warm Season Over the United States. Geophysical Research Letters, 45(16), 8586-8595. https://doi.org/10.1029/2018gl078465\u003c/li\u003e\n\u003cli\u003eMcPhaden, M. J., Zebiak, S. E., \u0026amp; Glantz, M. H. (2006). ENSO as an Integrating Concept in Earth Science. Science, 314(5806), 1740-1745. https://doi.org/10.1126/science.1132588\u003c/li\u003e\n\u003cli\u003eMINSA. (2025). Sala situacional de enfermedades metax\u0026eacute;nicas. Direcci\u0026oacute;n General de Epidemiolog\u0026iacute;a (DGE), Ministerio de Salud del Per\u0026uacute;, Retrieved July 2, 2025, from https://app7.dge.gob.pe/maps/sala_metaxenica/\u003c/li\u003e\n\u003cli\u003ePeng, Q., Xie, S., Passalacqua, G. A., Miyamoto, A., \u0026amp; Deser, C. (2024). The 2023 extreme coastal El Ni\u0026ntilde;o: Atmospheric and air-sea coupling mechanisms. Science Advances, 10(12). https://doi.org/10.1126/sciadv.adk8646\u003c/li\u003e\n\u003cli\u003eRam\u0026iacute;rez, I. J., \u0026amp; Briones, F. (2017). Understanding the El Ni\u0026ntilde;o Costero of 2017: The Definition Problem and Challenges of Climate Forecasting and Disaster Responses. International Journal Of Disaster Risk Science, 8(4), 489-492. https://doi.org/10.1007/s13753-017-0151-8\u003c/li\u003e\n\u003cli\u003eRichter, I., Stuecker, M. F., Takahashi, N., \u0026amp; Schneider, N. (2022). Disentangling the North Pacific Meridional Mode from tropical Pacific variability. Npj Climate And Atmospheric Science, 5(1). https://doi.org/10.1038/s41612-022-00317-8\u003c/li\u003e\n\u003cli\u003eRodgers, K. B., Lee, S., Rosenbloom, N., Timmermann, A., Danabasoglu, G., Deser, C., Edwards, J., Kim, J., Simpson, I. R., Stein, K., Stuecker, M. F., Yamaguchi, R., B\u0026oacute;dai, T., Chung, E., Huang, L., Kim, W. M., Lamarque, J., Lombardozzi, D. L., Wieder, W. R., \u0026amp; Yeager, S. G. (2021). Ubiquity of human-induced changes in climate variability. Earth System Dynamics, 12(4), 1393-1411. https://doi.org/10.5194/esd-12-1393-2021\u003c/li\u003e\n\u003cli\u003eRodr\u0026iacute;guez-Morata, C., D\u0026iacute;az, H. F., Ballesteros-Canovas, J. A., Rohrer, M., \u0026amp; Stoffel, M. (2018). The anomalous 2017 coastal El Ni\u0026ntilde;o event in Peru. Climate Dynamics, 52(9-10), 5605-5622. https://doi.org/10.1007/s00382-018-4466-y\u003c/li\u003e\n\u003cli\u003eRudloff, D., \u0026amp; L\u0026uuml;bbecke, J. F. (2025). What Controls the Evolution of Pacific Coastal Ni\u0026ntilde;o Events in the CESM Large Ensemble? Geophysical Research Letters, 52(9). https://doi.org/10.1029/2024gl113706\u003c/li\u003e\n\u003cli\u003eSmith, N., Kessler, W. S., Cravatte, S., Sprintall, J., Wijffels, S., Cronin, M. F., Sutton, A., Serra, Y. L., Dewitte, B., Strutton, P. G., Hill, K., Gupta, A. S., Lin, X., Takahashi, K., Chen, D., \u0026amp; Brunner, S. (2019). Tropical Pacific Observing System. Frontiers In Marine Science, 6. https://doi.org/10.3389/fmars.2019.00031\u003c/li\u003e\n\u003cli\u003eStott, P. A., Stone, D. A., \u0026amp; Allen, M. R. (2004). Human contribution to the European heatwave of 2003. Nature, 432(7017), 610-614. https://doi.org/10.1038/nature03089\u003c/li\u003e\n\u003cli\u003eTakahashi, K., \u0026amp; Mart\u0026iacute;nez, A. G. (2017). The very strong coastal El Ni\u0026ntilde;o in 1925 in the far-eastern Pacific. Climate Dynamics, 52(12), 7389-7415. https://doi.org/10.1007/s00382-017-3702-1\u003c/li\u003e\n\u003cli\u003eTakahashi, K., Montecinos, A., Goubanova, K., \u0026amp; Dewitte, B. (2011). ENSO regimes: Reinterpreting the canonical and Modoki El Ni\u0026ntilde;o. Geophysical Research Letters, 38(10), n/a. https://doi.org/10.1029/2011gl047364\u003c/li\u003e\n\u003cli\u003eTan, W., Hu, Z., McPhaden, M. J., Zhu, C., Li, X., \u0026amp; Liu, Y. (2024). On the Divergent Evolution of ENSO After the Coastal El Ni\u0026ntilde;os in 2017 and 2023. Geophysical Research Letters, 51(9). https://doi.org/10.1029/2024gl108198\u003c/li\u003e\n\u003cli\u003eTaschetto, A. S., Ummenhofer, C. C., Stuecker, M. F., Dommenget, D., Ashok, K., Rodrigues, R. R., \u0026amp; Yeh, S. (2020). ENSO Atmospheric Teleconnections. Geophysical Monograph, 309-335. https://doi.org/10.1002/9781119548164.ch14\u003c/li\u003e\n\u003cli\u003eTello, G. A. R., Takahashi, K., \u0026amp; Karamperidou, C. (2023). Explained predictions of strong eastern Pacific El Ni\u0026ntilde;o events using deep learning. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-45739-3\u003c/li\u003e\n\u003cli\u003eTimmermann, A., An, S., Kug, J., Jin, F., Cai, W., Capotondi, A., Cobb, K. M., Lengaigne, M., McPhaden, M. J., Stuecker, M. F., Stein, K., Wittenberg, A. T., Yun, K., Bayr, T., Chen, H., Chikamoto, Y., Dewitte, B., Dommenget, D., Grothe, P., . . . Zhang, X. (2018). El Ni\u0026ntilde;o\u0026ndash;Southern Oscillation complexity. Nature, 559(7715), 535-545. https://doi.org/10.1038/s41586-018-0252-6\u003c/li\u003e\n\u003cli\u003eTrenberth, K. E. (1997). The Definition of El Ni\u0026ntilde;o. Bulletin of the American Meteorological Society, 78(12), 2771-2778. https://doi.org/10.1175/1520-0477(1997)078\u0026lt;2771:TDOENO\u0026gt;2.0.CO;2\u003c/li\u003e\n\u003cli\u003eUnited Nations News. (2017, May 19). Peru: UN agency supports recovery of some 7,000 farmers from El Ni\u0026ntilde;o floods, landslides. https://news.un.org/en/story/2017/05/557772\u003c/li\u003e\n\u003cli\u003eWang, J., Kim, H., \u0026amp; DeFlorio, M. J. (2022). Future Changes of PNA-like MJO Teleconnections in CMIP6 Models: Underlying Mechanisms and Uncertainty. Journal Of Climate, 35(11), 3459-3478. https://doi.org/10.1175/jcli-d-21-0445.1\u003c/li\u003e\n\u003cli\u003eWei, Y., Mu, M., Lian, T., Hsu, H., Ren, H., Wang, J., Ji, C., \u0026amp; Tao, L. (2025). The March 2023 MJO and Its Impacts on the Subsequent Coastal El Ni\u0026ntilde;o. Journal Of Climate, 38(4), 1155-1173. https://doi.org/10.1175/jcli-d-24-0198.1\u003c/li\u003e\n\u003cli\u003eYglesias-Gonz\u0026aacute;lez, M., Vald\u0026eacute;s-Vel\u0026aacute;squez, A., Hartinger, S. M., Takahashi, K., Salvatierra, G., Velarde, R., Contreras, A., Mar\u0026iacute;a, H. S., Romanello, M., Paz-Sold\u0026aacute;n, V., Bazo, J., \u0026amp; Lescano, A. G. (2023). Reflections on the impact and response to the Peruvian 2017 Coastal El Ni\u0026ntilde;o event: Looking to the past to prepare for the future. PLoS ONE, 18(9), e0290767. https://doi.org/10.1371/journal.pone.0290767\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Coastal El Niño, climate change, Global Climate Models, Marine Heat Waves, Tropical Pacific","lastPublishedDoi":"10.21203/rs.3.rs-7774655/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7774655/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoastal El Ni\u0026ntilde;o events in the Eastern Boundary Upwelling system off Peru have garnered significant attention due to their substantial societal impacts. The recent events, 2017 and 2023, rank amongst the strongest on record, raising concerns about their future behavior. This study relies on the CESM2 Large Ensemble (CESM2-LE) to explore how the frequency, intensity and spatial patterns of coastal events may evolve throughout the 21st century. Initially, an evaluation of the model revealed a pattern bias associated with a too energetic South Pacific Meridional Mode (SPMM) and a weaker North Pacific Meridional Mode (NPMM), both patterns known to affect coastal warming. Nevertheless, the model realistically simulates precipitation during coastal events in both their cold and warm phases and captures a strong link to Pacific Meridional Modes (PMMs). At the end of the 21st century, warm coastal events are expected to become 40% less frequent but are associated with a precipitation increase of approximately 2 mm/day due to increased sea surface temperatures in the mean state. Future climatological precipitation levels during February-March-April (FMA) from the third decade of the 21st century onward are projected to match those currently seen during extreme events, such as the 2017 Coastal El Ni\u0026ntilde;o episode. Coastal La Ni\u0026ntilde;a, conversely, exhibits no meaningful change in frequency or intensity, but may serve as intervals of moderate rather than extreme precipitation in the future.\u003c/p\u003e","manuscriptTitle":"Coastal El Niño and La Niña Events in a Changing Climate: Insights from the CESM2 Large Ensemble","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 11:10:55","doi":"10.21203/rs.3.rs-7774655/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2025-12-20T00:54:15+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-10-22T07:04:03+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-21T00:26:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-08T05:34:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Climate Dynamics","date":"2025-10-06T13:19:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c513a91a-1b71-4dc9-b350-945311c81345","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-01T02:48:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-31 11:10:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7774655","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7774655","identity":"rs-7774655","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}