The findings carry weight well beyond the two bat species themselves. Frugivorous phyllostomid bats are keystone agents of forest regeneration, moving seeds across fragmented landscapes, pollinating chiropterophilous plants, redistributing nutrients through guano deposits, and maintaining genetic connectivity among plant populations. If V. caraccioli contracts across the Amazon, the study’s authors warn, the disruption could ripple through seed-dispersal networks and compromise the natural regeneration of one of the planet’s most biodiverse ecosystems. Both species are currently listed as Least Concern by the IUCN, but the researchers argue that this label, based largely on broad geographic ranges, may mask vulnerabilities that only become visible when climate data are brought into the picture.

The genus Vampyrodes has long been a taxonomic puzzle. Originally proposed as a subgenus in 1889 and later treated as a single species with two subspecies, it was only in 2011 that phylogenetic analysis of mitochondrial cytochrome b gene sequences, combined with a comprehensive review of cranial morphology, confirmed that V. major and V. caraccioli are distinct species. V. major occupies Central America from southern Mexico into Colombia, while V. caraccioli ranges across northern and eastern South America, dominating in the Amazon basin, the Andean foothills, and tropical southern Brazil. Because these bats depend on intact forest for both food and roosting sites, they have traditionally been treated as bioindicators of well-preserved forest, although recent records from restoring secondary vegetation hint that their tolerance may be broader than once assumed.

To build their models, the researchers compiled 190 verified occurrence records, 77 for V. major and 113 for V. caraccioli, drawn from the Global Biodiversity Information Facility and the published literature, deliberately excluding citizen-science observations because the cryptic morphology of these bats raises the risk of misidentification in non-vouchered records. The team filtered the data to remove duplicates and non-georeferenced points, then thinned the records spatially to a minimum separation of ten kilometers to reduce autocorrelation. Fifteen bioclimatic variables from the WorldClim 2.1 database were screened, with four excluded due to geographic discontinuities, and a Spearman correlation threshold of 0.75 narrowed the predictor set to six variables capturing annual mean temperature, temperature seasonality, temperature annual range, warmest-quarter temperature, annual precipitation, and wettest-month precipitation.

The modeling itself used the Maxent algorithm, implemented through the Wallace platform in R, with 120 candidate parameter combinations tested per species. The final model for V. major relied on linear features with a regularization multiplier of one, while V. caraccioli required a more complex linear-quadratic-hinge configuration with a multiplier of three. Model selection followed established statistical criteria, requiring omission rates below five percent and minimizing the corrected Akaike information criterion. Variable importance outputs revealed telling differences: temperature annual range dominated the V. major model at 67.9 percent permutation importance, whereas V. caraccioli’s suitability hinged primarily on annual precipitation at 37.2 percent, followed by temperature annual range and annual mean temperature. In other words, the Central American species is chiefly constrained by thermal variability, while its Amazonian relative is governed by moisture availability.

Niche comparisons in multivariate environmental space showed that the two species share broad climatic ground but are not identical. The first two principal components explained 75.7 percent of environmental variance, and the observed overlap was moderate, with Schoener’s D at 0.398 and the Hellinger-based I at 0.575. Randomization tests found no evidence that similarity exceeded what background environments would predict, and equivalency could not be strictly rejected, but the niche centroids were measurably displaced and the non-overlapping components were asymmetric. The authors interpret this pattern as partial niche conservatism: a shared evolutionary climatic background, consistent with the genus’s recent crown age of roughly two million years, superimposed with early differentiation along axes of temperature seasonality and dry-season moisture. A companion study published in 2026 reached a similar conclusion, linking seasonal climatic gradients to morphological divergence between the two species.

The future projections are where the story turns stark. The team ran an ensemble of five CMIP6 general circulation models under two Shared Socioeconomic Pathways for the period 2041 to 2060: SSP1-2.6, a sustainability-oriented scenario limiting warming to below two degrees, and SSP5-8.5, a fossil-fuel-intensive pathway with substantially higher warming. For V. major, roughly 98 percent of currently suitable area remained stable under both scenarios, with gains and losses each staying below two percent. V. caraccioli told a different story: suitable area shrank by about 44 percent under the low-emission scenario and 51 percent under the high-emission scenario, with the losses concentrated across the Amazon basin in Brazil, Colombia, Venezuela, Guyana, and Suriname. Multivariate environmental similarity surfaces confirmed that most projections fell within the range of training conditions, lending credibility to the forecasts, though the authors caution that extrapolation risk is not uniform across Amazonia.

The protected-area analysis added a sobering dimension. Under current conditions, about 25.5 percent of V. caraccioli’s climatically suitable habitat, roughly 91,217 square kilometers, falls within protected areas, mostly in the Amazon. By mid-century, that figure collapses: protected suitable area drops to about 48,517 square kilometers under SSP1-2.6 and 39,795 square kilometers under SSP5-8.5, meaning more than 46 percent of the species’ current protected habitat could lose its climatic suitability. V. major, by contrast, holds steady, with around 20.5 percent of its suitable area within protected zones across all scenarios. The message is clear: existing reserves, however crucial, may be insufficient to safeguard V. caraccioli if climate and land-use changes continue unabated, and static protected-area networks cannot track dynamically shifting climates.

The authors are careful to note the limitations of their approach. Macroclimatic niche models capture regional suitability but miss fine-scale determinants such as roost microclimates, cave and cavity buffering, riparian corridors, and topography-driven microrefugia that can decouple local conditions from broad climate layers. Occurrence records for V. caraccioli also remain sparse across large portions of Amazonia, meaning the projected contractions could be conservative if suitable climates exist in poorly sampled subregions. Bats’ strong attachment to home ranges and roosts may further limit their ability to track shifting suitability through dispersal, since long-distance migration is restricted to a subset of species. The team recommends pairing multi-season fieldwork on roost and foraging ecology with population genomics to identify locally adapted, climate-resilient populations, an approach shown in other systems to reduce predicted range losses.

The conservation prescriptions that emerge from the study are deliberately species-specific. For V. caraccioli, the priorities are strengthening protected-area networks, restoring degraded habitats, and designing ecological corridors that connect humid forest zones across the Amazon, allowing bats to move toward microclimatic refugia while preserving the seed-dispersal services on which forest regeneration depends. For V. major, climatic stability should not be mistaken for invulnerability: ongoing deforestation and agricultural expansion across Central America continue to fragment habitats and erode connectivity, so corridor networks and intact-forest preservation remain urgent there as well. Because suitable areas and potential refugia span national borders, the authors argue that coordinated transboundary planning will be essential. In a warming world where the Amazon has just recorded unprecedented drought and warmth, the fate of these unassuming seed dispersers may serve as an early warning for the mutualistic webs that hold tropical forests together.

Subject of Research: Climate-driven habitat suitability and conservation of Vampyrodes fruit bats in the Neotropics

Article Title: Habitat characterization and climate-driven niche shifts of Vampyrodes bats reveal contrasting futures for V. major and V. caraccioli

Article References: Hernández-Rodríguez, S., Martínez-Borrego, D., Jácome-Flores, M., & Cruz, D. D. (2026). Habitat characterization and climate-driven niche shifts of Vampyrodes bats reveal contrasting futures for V. major and V. caraccioli. Web Ecology, 26(2), 157-173. https://doi.org/10.5194/we-26-157-2026

Image Credits: AI Generated

DOI: 10.5194/we-26-157-2026

Keywords: Vampyrodes, fruit bats, ecological niche modeling, climate change, seed dispersal, Amazon basin, Maxent, protected areas, Neotropics, habitat loss, SSP scenarios, conservation corridors

The first of the two simulations is a historical reconstruction. The team took the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis, known as ERA5, which blends observations with a global forecast model to create a physically consistent record of the recent past, and used it to drive the Weather Research and Forecasting model, or WRF, over the full South American domain for the period 2000 to 2021. Because the model grid is only 4 kilometers across, deep moist convection, the process that builds towering cumulonimbus clouds and produces intense rain, hail, and lightning, can be resolved directly by the model’s dynamics. This matters because traditional climate models must parameterize convection, and those parameterizations are a leading source of error in simulating the timing, intensity, and location of rainfall, particularly over complex terrain like the Andes and over the Amazon, where the diurnal cycle of convection is notoriously difficult to capture.

To test whether the simulation was trustworthy, the researchers validated it against a battery of independent satellite and reanalysis products covering precipitation and near-surface air temperature. The results were striking: the 4-kilometer simulation captured South America’s climate across an enormous range of scales, from the slow interannual variations tied to phenomena like the El Niño-Southern Oscillation, through the annual march of the South American monsoon, down to the hour-by-hour rhythm of afternoon thunderstorms. Crucially, the downscaling demonstrated clear added value over the original ERA5 data itself, especially at daily to sub-daily time scales and at mesoscale to local spatial scales. In other words, the fine-grid model did not merely reproduce its coarse driving data; it genuinely improved the representation of the processes that matter most for floods, droughts, agriculture, and water management.

The second simulation looks toward the end of the century using an elegant technique called the Pseudo-Global Warming, or PGW, approach. Rather than running a full coupled climate model into the future, the team kept the historical weather sequence from ERA5 but perturbed the meteorological fields with monthly climate change signals drawn from the Community Earth System Model Large Ensemble, known as LENS2. The perturbations correspond to roughly 3 degrees Celsius of global warming above preindustrial levels under the CMIP6 SSP3-7.0 emissions scenario, representative of conditions expected during the 2060 to 2080 period. The advantage of this method is that the future simulation experiences the same weather patterns as the historical one, only embedded in a warmer atmosphere, which makes differences between the two runs directly attributable to the climate change signal rather than to the chaos of natural variability.

The projections that emerge are sobering. The PGW simulation shows significant continental-scale warming, with the strongest temperature increases concentrated over the Andes, broadly consistent with the underlying LENS2 projections. This elevation-dependent warming in the Andes carries particular weight because the region’s snowpack and glaciers act as natural water towers for millions of people downstream. Indeed, the simulation projects declines in both snowfall and snowpack across the Andes, a finding that aligns with a growing body of observational evidence of shrinking snow persistence and retreating cryosphere in the region, and one with direct implications for the water supplies of cities such as Santiago, Lima, and Bogotá.

Precipitation changes prove far more spatially and seasonally complex than the temperature signal. Most regions of the continent experience either modest increases or slight decreases in total rainfall, but one region stands out: the Amazon Basin exhibits predominant drying during the austral summer, the season when the rainforest’s own convective recycling normally sustains the wet season. This projected summer drying echoes concerns raised by earlier studies about the strengthening of the Amazonian dry season and the vulnerability of the forest to a self-reinforcing cycle of drought, fire, and degradation. Because the Amazon recycles a large fraction of its own rainfall and exports moisture eastward and southward through low-level jets often described as aerial rivers, changes in the basin’s water balance can propagate far beyond its borders, affecting agriculture in the La Plata basin and beyond.

Perhaps the most robust and consequential finding concerns extremes. Across the continent, extreme precipitation intensifies in the warmer climate, consistent with the basic thermodynamic principle that a warmer atmosphere holds roughly 7 percent more water vapor per degree of warming, loading the dice toward heavier downpours even where mean rainfall changes little. For a continent already familiar with devastating floods in southeastern Brazil and Argentina, gargantuan hailstorms on the Argentine pampas, and deadly landslides in the Andean foothills, an intensification of hourly rainfall extremes represents a substantial escalation of hazard. The convection-permitting framework is especially valuable here, because it resolves the mesoscale convective systems, vast organized storm complexes that can span hundreds of kilometers, responsible for much of South America’s extreme rainfall and severe weather.

The dataset itself, dubbed SAAG, is openly available through the NCAR GDEX system and the Chilean National Laboratory HPC system, but its sheer scale presents a paradox. The raw WRF output for both the historical and future simulations amounts to approximately 2 petabytes, far too large for individual research groups to copy locally. The authors argue that establishing centralized, high-performance analysis infrastructure in South America, comparable to platforms such as the UK Met Office’s JASMIN or Germany’s Levante system, is urgently needed. Without such infrastructure, they caution, datasets of this magnitude cannot fully support the climate science, impact studies, and policymaking in the very region they describe, a gap that highlights persistent inequities in global scientific computing capacity.

For South American scientists, water managers, and policymakers, the significance of this work is difficult to overstate. The continent hosts the planet’s largest tropical forest, its driest nonpolar desert, and mountain ranges whose snow and ice sustain major river systems, all within a single modeling domain that can now be examined at storm scale over multiple decades. The simulations open the door to detailed studies of atmospheric convection, land-surface feedbacks, moisture recycling, and hydrological processes, both in the present climate and under a substantially warmer one. As the world approaches the warming levels projected in these runs, the SAAG dataset offers the most detailed window yet into how South America’s extraordinary climates, and the hundreds of millions of people who depend on them, may be transformed in the decades ahead.

Subject of Research: Convection-permitting dynamical downscaling of present and future South American climate at 4-km resolution

Article Title: Multi-decadal convection permitting dynamical downscaling of current and future climates over South America

Article References: Liu, C., Ikeda, K., Dominguez, F., Prein, A. F., Rasmussen, R. M., Zhang, Z., Xue, L., Neale, R. B., Chun, K. P., He, C., Rios-Berrios, R., Reboita, M. S., Huang, Y., Gomes, H. B., Llopart, M., Dudhia, J., Tian, Y., Scaff, L., Varble, A. C., … Gutmann, E. D. (2026). Multi-decadal convection permitting dynamical downscaling of current and future climates over South America. Climate Dynamics, 64(10), Article 435. https://doi.org/10.1007/s00382-026-08376-w

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08376-w

Keywords: South America, dynamical downscaling, convection-permitting modeling, WRF model, pseudo-global warming, climate change, Amazon Basin, Andes, extreme precipitation, snowpack, ERA5, Climate Dynamics

The stakes of this kind of evaluation are far higher than an academic exercise in scorekeeping. Global climate models are the engines that generate the projections used by the Intergovernmental Panel on Climate Change, and they also supply the boundary conditions that drive regional climate models in so-called dynamical downscaling. If a global model misrepresents the circulation patterns, moisture transport or precipitation regimes of a particular region, every downstream projection inherits those errors. For South America, a continent that contains the Amazon rainforest, the vast wetlands of the La Plata Basin, the semiarid interior of northeastern Brazil and the dramatic seasonal reversal of the South American Monsoon, the choice of which model to trust is not a technical footnote. It shapes estimates of future water availability, agricultural viability, forest resilience and flood risk for hundreds of millions of people.

The research team framed their assessment around the historical period from 1979 to 2014, comparing each model’s simulations against ERA5, the state-of-the-art atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts. Reanalyses blend observations with numerical weather prediction to create a physically consistent record of the past climate, and ERA5 has become a standard benchmark for model evaluation worldwide. By measuring how closely each CMIP6 model tracks ERA5 across multiple atmospheric variables and vertical levels, the researchers could quantify not just whether a model gets surface temperature roughly right, but whether it captures the three-dimensional structure of the atmosphere—the winds aloft, the geopotential heights, the vertical distribution of moisture—that governs how weather systems actually form and evolve over the continent.

What makes the study genuinely distinctive is its multilevel design. Rather than confining the analysis to a single variable at the surface, the authors examined the atmosphere at different heights, recognizing that a model can simulate plausible surface conditions while simultaneously botching the upper-level circulation that steers storms and organizes convection. This vertical dimension matters enormously for South America, where the Bolivian High, an anticyclone that forms aloft during the austral summer, is intimately linked to the monsoon system and to deep convection over the Amazon. A model that fails to reproduce upper-level circulation may still appear competent in surface statistics while getting the fundamental dynamics wrong—a trap that single-variable evaluations routinely miss.

To make the comparison tractable, the researchers divided South America into five subdomains that capture the continent’s principal climatic regimes: the northern Amazon Basin, the southern Amazon Basin, the Northeast region of Brazil, the South American Monsoon domain, and the La Plata Basin. Each of these regions poses its own modeling challenge. The Amazon tests how well a model handles the coupling between forest, soil moisture and deep convection in a regime where precipitation is largely recycled from evapotranspiration. Northeast Brazil, with its semiarid climate punctuated by extreme droughts, probes the representation of the intertropical convergence zone and its seasonal migrations. The monsoon domain demands that a model capture a full seasonal cycle of onset, maturity and demise, while the La Plata Basin, one of the world’s great agricultural heartlands, is notorious for organized mesoscale convective systems that produce some of the most intense rainfall events on the planet.

Across these regions and variables, the team deployed a battery of statistical metrics—measures of bias, spatial pattern correlation, and the ability to reproduce observed variability—and then synthesized them through a comprehensive rating index, or CRI. This framework condenses many separate performance measures into a single score for each model in each region, allowing a consistent ranking that would otherwise be impossible when different metrics point in different directions. The approach addresses a long-standing problem in climate science: model evaluation often produces contradictory conclusions depending on which metric an analyst happens to emphasize, and a transparent, integrated index reduces the subjectivity inherent in model selection. It also echoes a broader movement in the field, reflected in recent benchmarking literature, toward systematic, multi-metric assessment rather than ad hoc comparisons.

The results reveal a striking geographic pattern. Model performance diverges most strongly in the tropical subdomains, where the interplay of convection, land surface feedbacks and large-scale circulation creates a punishing test for coarse-resolution global models. In the subtropical regions, by contrast, the models show considerably higher consistency with one another and with the reanalysis, suggesting that the large-scale dynamics there are more robustly captured. This dispersion in the tropics is a familiar refrain in climate modeling—convection remains one of the hardest processes to represent because it occurs at scales far smaller than a typical model grid cell and must be approximated through parameterizations—but the study quantifies just how much this limitation separates individual models from one another over South America.

At the top of the rankings, four models distinguish themselves. CNRM-CM6-1, developed by the French research community; GFDL-ESM4, from the United States Geophysical Fluid Dynamics Laboratory; MPI-ESM1-2-HR, the high-resolution configuration of the Max Planck Institute Earth System Model; and NorESM2-MM, the medium-resolution variant of the Norwegian Earth System Model, all deliver superior performance across multiple variables and multiple regions. Their common thread is instructive: several of these models incorporate relatively sophisticated atmospheric components or finer horizontal resolution, which helps them resolve the terrain, coastlines and circulation features that shape South American climate. At the other end of the spectrum, INM-CM4-8, KIOST and NESM3 show persistent limitations, particularly in simulating upper-level circulation and the precipitation processes that depend on it. The authors emphasize that these weaker models are not without value in a global context, but their specific failures over South America make them poor candidates for regional applications on this continent.

Perhaps the most practically important finding is that the best-ranked models also reproduce the observed monthly precipitation climatology and its variability more faithfully than their lower-ranked counterparts. Precipitation is the variable that matters most for water resources, hydropower and agriculture in South America, and it is notoriously difficult to simulate because it depends on the delicate interaction of moisture convergence, atmospheric stability and convection. The fact that the comprehensive rating index successfully identifies models that excel specifically in precipitation suggests that the framework captures something real about model quality rather than merely rewarding statistical conformity in easier variables. The authors explicitly position their ranking as a foundation for selecting boundary conditions for dynamical downscaling over South America, giving regional modeling groups a defensible, evidence-based starting point for their experiments.

The broader lesson of the study is one that climate scientists have been articulating with increasing urgency: there is no universally best climate model, and ensembles should not be treated as interchangeable collections of equals. A model that performs admirably over Europe or East Asia may stumble over the Amazon, and a model that excels at surface temperature may misrepresent the winds five kilometers above the surface. The authors’ central conclusion—that models should be chosen based on their demonstrated performance in the region of interest—represents a shift from the traditional practice of using all available models indiscriminately toward a more discriminating, regionally informed selection strategy. For a continent whose climate future hinges on the fate of the Amazon forest, the reliability of the monsoon and the behavior of the La Plata Basin’s rivers, knowing which models deserve a seat at the table is not a luxury. It is the difference between projections that inform sound adaptation policy and projections that quietly mislead it.

Subject of Research: Evaluation of CMIP6 global climate model performance over South America

Article Title: Assessment of CMIP6 models’ performance for South America: multilevel perspective

Article References: Assessment of CMIP6 models’ performance for South America: multilevel perspective. (n.d.). https://doi.org/10.1007/s00704-026-06585-1

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06585-1

Keywords: CMIP6, climate models, South America, ERA5, precipitation, Amazon Basin, South American Monsoon, La Plata Basin, model evaluation, dynamical downscaling, reanalysis, climate projections

The research team, led by G. Botetano and including J. C. Espinoza, K. Takahashi, and colleagues from institutions in Peru, France, and Brazil, analyzed the period from 1982 to 2024, using satellite-derived precipitation from CHIRPS, temperature from the ERA5-Land reanalysis, sea surface temperatures from HadISST, and more than forty years of daily water level measurements from the Negro River gauge at the port of Manaus. The Manaus gauge is no ordinary data point. Because the Negro River responds to rainfall across roughly three million square kilometers of upstream Amazonia, its levels integrate the hydrological memory of the entire northwestern basin, lagging behind precipitation by about three months as floodplains and saturated soils buffer and release water.

The technical core of the study rests on empirical orthogonal function analysis, a statistical technique that decomposes complex climate fields into their dominant spatial patterns and corresponding time series. When the researchers applied this method to seasonal precipitation, temperature, and terrestrial water storage anomalies measured by the GRACE and GRACE-FO satellite gravimetry missions, they found something remarkable: the leading modes of all these variables were tightly coupled. Precipitation variability correlated with water storage at around 0.78 to 0.79, river levels tracked precipitation at roughly 0.76 to 0.81, and temperature—the sign-inverted, since heat accompanies drought—correlated with storage at 0.76 to 0.88. In other words, the Amazon does not experience a rainfall anomaly here and a temperature anomaly there; it moves as a coherent compound system.

AB-CHEX distills this coupling into a single number for each season. Negative phases below the 15th percentile mark dry–hot compound events, combining rainfall deficits, anomalous warmth, and falling rivers. Positive phases above the 85th percentile mark wet–cold events, the signature of the great floods. The index validated beautifully against history: it reproduces the documented droughts of 1998, 2005, 2010, 2015–16, and 2023–24, and the floods of 2009, 2012, and 2021–22. It also captures basin-scale water storage variability, correlating with the GRACE-derived storage component at 0.93 in the wet season and 0.88 in the dry season—evidence that the index reflects genuine basin-wide hydroclimatic dynamics rather than the quirks of any single dataset.

Then comes the headline finding. When the researchers constructed a trend-preserving version of the index, AB-CHEXLT, the wet and dry seasons revealed opposite long-term trajectories. During the extended wet season from November to April, the index shows a significant drift toward wetter and colder compound conditions, consistent with an intensifying hydrological cycle and the record flooding that has plagued northern Amazonia—the cumulative duration of flood emergencies at Manaus in the first two decades of this century already exceeds that of the entire twentieth century by about 20 percent. During the dry season from June to September, the index shows a sustained shift toward increasingly dry–hot conditions, and here the decomposition is damning: the linear trend explains nearly a quarter of total dry-season variance, and within that trend, warming alone accounts for 86.9 percent of the long-term signal.

And then there is 2024. Even after removing long-term trends, the AB-CHEX value for 2024 reached its lowest magnitude across both seasons in the entire 43-year record, marking an event the authors describe as unprecedented. The 2023–24 drought combined delayed rainfall onset, record-breaking heat, collapsing river levels, widespread canopy browning, and sharp declines in gross primary productivity, with pronounced soil moisture deficits and elevated vapor pressure deficit covering more than half the Amazon region during the austral springs of 2023 and 2024. Previous severe dry–hot events clustered in recognizable patterns—the wet-season extremes of 1998 and 2016 during strong El Niño years, the dry-season extremes of 2005, 2010, and 2023—but 2024 broke the mold in both seasons simultaneously.

To find the culprits, the team turned to composite analysis of atmospheric circulation and sea surface temperatures during extreme index phases. During the wet season, the story is fundamentally Pacific. Dry–hot years show pronounced mid-level subsidence between 300 and 600 hectopascals across equatorial Amazonia—a weakening of the ascending branch of the regional Hadley circulation that suppresses deep convection—paired with an eastward shift of the Walker-like zonal circulation that enhances ascent over the eastern Pacific. Sea surface composites reveal the classic El Niño fingerprint: significant warming over the Niño-3.4 region. Quantitatively, ENSO-related Pacific sea surface temperature anomalies explain 61 percent of wet-season AB-CHEX variability, while the regional Hadley circulation index alone correlates at 0.85, accounting for 72 percent. El Niño weakens the overturning cells and starves the basin of moisture; La Niña strengthens them, driving the great wet–cold floods.

The dry season tells a different and, in a warming world, more troubling story. Here the primary control is not the Pacific but the Caribbean–Tropical North Atlantic region. Dry–hot years show intensified subsidence between 500 and 800 hectopascals, a northward-shifted and strengthened descending branch of the regional Hadley cell, and marked warming over the Caribbean–TNA. Warm sea surface anomalies there weaken the meridional pressure gradient, slacken the trade winds, and choke off the Atlantic moisture inflow that sustains dry-season convection. The Caribbean–TNA sea surface temperature index correlates with dry-season AB-CHEX at 0.79, explaining 62 percent of its variability—and crucially, this relationship holds regardless of the Pacific state, remaining significant under warm, neutral, and cold Niño-1+2 conditions. When the researchers removed the shared variance between the two ocean basins using partial correlations, the Pacific influence on dry-season compound extremes vanished entirely, while the Caribbean–TNA influence on wet-season extremes persisted only as a secondary modulation of ENSO-driven variability.

The spatial consequences of these two forcing regimes differ profoundly. During wet-season dry–hot events, precipitation deficits exceeding 25 percent and temperature anomalies above 1.2 degrees Celsius concentrate over northeastern Amazonia, where ENSO’s Walker and Hadley circulation teleconnections bite hardest. Dry-season dry–hot events, driven by the more spatially uniform Caribbean–TNA forcing, produce rainfall reductions exceeding 45 percent and anomalies near plus one degree Celsius across the Peruvian, central, and southern Amazon—wider, deeper, and more severe. Land–atmosphere feedbacks amplify the damage: antecedent soil moisture deficits persist through hydrological memory, suppressing evapotranspiration and shifting the surface energy balance toward sensible heating, which reinforces both the warmth and the rainfall deficit. In total, roughly 68 percent of the basin exhibits significant compound coupling with the index during the wet season, rising to 76 percent during the dry season.

The implications reach beyond atmospheric science. The sustained warming of the Caribbean–TNA since the mid-2000s, the authors note, appears to be intensifying dry–hot compound conditions, strengthening land–atmosphere feedbacks, and amplifying hydroclimatic instability—layered on top of deforestation-driven delays in wet-season onset across southern Amazonia and rising tree mortality across the basin. Because all of AB-CHEX’s inputs are publicly available, routinely updated datasets, the index can be computed operationally by hydrometeorological services and early-warning centers across Amazonian countries, with the caveat that the two-to-three-month hydrological lag limits real-time applications. What the index makes unmistakably clear is that the Amazon’s future hinges on two oceans and a warming atmosphere acting in concert—and that the dry season, where warming already explains most of the long-term drift toward compound drought and heat, is where the rainforest’s margin of safety is eroding fastest.

Subject of Research: Large-scale ocean–atmosphere drivers of compound drought–heat and flood–cold extremes in the Amazon Basin from 1982 to 2024

Article Title: Large-scale climate drivers of extreme compound events in the Amazon from interannual to long-term timescales (1982–2024)

Article References: Botetano, G., Espinoza, J. C., Takahashi, K., Gutierrez Villarreal, R. A., Marengo, J., & Wongchuig, S. (2026). Large-scale climate drivers of extreme compound events in the Amazon from interannual to long-term timescales (1982–2024). Climate Dynamics, 64(10), Article 427. https://doi.org/10.1007/s00382-026-08382-y

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08382-y

Keywords: Amazon Basin, compound extremes, drought, ENSO, El Niño, Tropical North Atlantic, Caribbean sea surface temperature, Hadley circulation, Walker circulation, river levels, GRACE, hydroclimatic variability