The core insight behind the approach is that regional rainfall is not random noise. It is strongly steered by a handful of recurring pressure patterns over the North Atlantic, most famously the North Atlantic Oscillation, or NAO, which describes the seesaw in sea-level pressure between the Azores High and the Icelandic Low. When the NAO is in one phase, the jet stream and storm tracks shift, drenching some parts of Europe and leaving others dry. Because the ocean evolves far more slowly than the atmosphere, these slowly varying modes carry a degree of memory that initialized decadal climate predictions, which start from the observed state of the ocean and atmosphere and run forward for ten years, can in principle capture. Previous work by Smith and colleagues in 2020 showed that the NAO is far more predictable than climate models superficially suggest, and that selecting model simulations whose NAO matches a skillful NAO forecast can dramatically improve winter rainfall predictions over Europe.

The new study builds on a 2026 refinement by Alkama and colleagues that fixes a subtle but damaging timing flaw in earlier NAO forecasts. When multi-model decadal hindcasts were averaged across consecutive start dates, a systematic two-year lag crept into the predictions. By restricting the averaging to hindcast years that actually fall within each target eight-year window, and then rescaling the forecast variance to correct for the well-known signal-to-noise paradox, in which ensemble means underestimate the true amplitude of climate signals, the boosted winter NAO forecast improved its anomaly correlation coefficient from 0.58 to 0.83, a striking gain in predictive skill.

But the French team did not stop at the NAO. Using empirical orthogonal function analysis and wavelet analysis of the high-resolution SAFRAN observational dataset, which provides precipitation over France on an 8-kilometer grid going back to 1958, they identified the dominant modes of rainfall variability in both winter and summer. Winter precipitation turned out to be governed by two main patterns: a spatially coherent mode explaining just over half the variance, tied to the West Europe Pressure Anomaly index, and a north-south dipole linked to the winter NAO. Wavelet analysis revealed statistically significant periodicities of roughly six, eight, and twenty-one years, hinting at an oceanic imprint on French rainfall. In summer, the leading mode correlated strongly with the Summer North Atlantic Oscillation, while a newly defined Mediterranean-Scandinavia index captured the second dipole mode.

The heart of the method is a subsampling procedure applied to a 32-member uninitialized ensemble of extended historical simulations from the IPSL-CM6A-LR climate model. For each eight-year window, the boosted decadal forecast of the chosen index is compared with the index values simulated by each of the 32 members, and the five members whose large-scale circulation best matches the prediction are selected. These five simulations then provide the precipitation forecast, on the logic that their atmospheric states are dynamically consistent with the predicted phase of the NAO or other index. Crucially, because the members come from an uninitialized ensemble, the method sidesteps the initial drift that plagues directly initialized decadal predictions in their first years. For summer forecasts, the team combined the Summer NAO with an unadjusted Atlantic Multidecadal Variability index, retaining the externally forced warming trend in North Atlantic sea surface temperatures so that the selection could constrain both internal variability and the forced signal.

The results are impressive for winter. Subsampling on the winter NAO produced significant anomaly correlation coefficients across 78 percent of French grid cells, covering nearly all of mainland France except the Mediterranean coast, with a median correlation of 0.54. Residual correlation analysis confirmed that this skill goes beyond what the forced, externally driven response alone can explain, and probabilistic skill scores beat the full uninitialized ensemble over 61 percent of the territory. Around Rouen in northern France, correlations with observed eight-year precipitation anomalies improved from 0.68 to 0.83 compared with the uninitialized ensemble mean. Winter forecasts based on the West Europe Pressure Anomaly were also skillful, though less so, and the authors trace this paradox to a northward shift of the pressure centers in the IPSL model, which pushes the simulated WEPA influence toward Britain and Ireland rather than France.

Summer proved harder, as expected. Summer rainfall over France is shaped more by localized convective processes that coarse-resolution models struggle to represent, and the summer pressure indices fluctuate within a much narrower range of roughly one hectopascal, compared with four for the winter NAO. Subsampling on the Summer NAO alone yielded significant skill over only 12 percent of the country. But adding the Atlantic Multidecadal Variability index as a joint constraint nearly quadrupled the skillful area to 48 percent of France, with a median correlation of 0.32, and delivered significant probabilistic gains over 39 percent of grid cells. In the Rouen region, summer correlations jumped from essentially zero to 0.41. The oceanic index acts as a filter, weeding out ensemble members whose sea surface temperature state is dynamically inconsistent with the predicted phase, even though the index itself is not used as a direct rainfall predictor.

To make the forecasts usable at the local scale, the team applied the CDF-t statistical downscaling method, a quantile-mapping technique that adjusts the distribution of model precipitation to match observations while preserving large-scale variability. This transformed the roughly 150-kilometer model output into bias-corrected forecasts on the 8-kilometer SAFRAN grid, the same resolution used by French hydrological services and impact studies. Skill evaluations against both SAFRAN and the ERA5 reanalysis, using block bootstrap significance testing and false discovery rate correction, showed that the subsampled winter forecasts substantially outperform the raw multi-model DCPP ensemble mean, whose skill over France was largely confined to the Atlantic coast.

The framework is deliberately designed to be transferable. The authors emphasize that the same five-step workflow, identifying relevant indices, boosting their decadal forecasts, subsampling a large ensemble, downscaling, and evaluating skill, could be applied to other regions and other variables, provided suitable large-scale predictors and uninitialized ensembles exist. Sensitivity tests showed the method remains meaningful with five-year rather than eight-year averages, and that five members is an optimal sub-ensemble size balancing correlation against uncertainty estimation. Limitations remain, including the coarse resolution of the uninitialized ensemble, the single-model design, and the weaker predictability of some summer indices, but perfect-prediction experiments suggest that any improvement in index forecasts would translate directly into better regional rainfall predictions.

For a country where rainfall governs hydropower output, agricultural planning, and the management of increasingly stressed water resources, the arrival of skillful decadal precipitation forecasts at municipal scale is more than an academic milestone. It offers water managers and climate services a genuinely new tool: a physically grounded, decade-ahead outlook that captures the rhythm of natural Atlantic variability rather than merely the long march of greenhouse warming. As larger ensembles and improved index predictions become available, this dynamically consistent subsampling strategy could well become a standard component of operational decadal climate prediction across Europe and beyond.

Subject of Research: Decadal precipitation forecasting over France using dynamically consistent subsampling of climate model ensembles based on predicted North Atlantic circulation indices

Article Title: Toward robust fine-scale decadal precipitation forecasts through dynamically consistent subsampling

Article References: Toward robust fine-scale decadal precipitation forecasts through dynamically consistent subsampling. (n.d.). https://doi.org/10.5194/esd-17-1177-2026

Image Credits: AI Generated

DOI: 10.5194/esd-17-1177-2026

Keywords: decadal prediction, precipitation, North Atlantic Oscillation, Atlantic Multidecadal Variability, subsampling, France, climate services, downscaling, sea-level pressure, CMIP6, hydroclimate, water resources

The research team, led by Kutubuddin Ansari and Emine Tanır Kayıkçı of the Department of Geomatics Engineering at Karadeniz Technical University in Trabzon, approached the problem with a deliberately standardized methodology. Rather than comparing raw temperatures or rainfall totals, which vary enormously between a coastal station on the Aegean and a high plateau site in eastern Anatolia, the authors converted every measurement into a standardized anomaly using Z-transformation. This statistical technique expresses each daily or annual value as a departure from the long-term mean, scaled by the standard deviation of the record at that station. The result is a dimensionless quantity that allows a fair comparison of climate behavior across radically different geographic settings, from the humid Black Sea coast to the semi-arid interior of Central Anatolia.

With the anomalies in hand, the researchers computed long-term trends for four surface variables: temperature, sea-level pressure, wind speed and precipitation. The signal that emerges most clearly from the data is a nationwide warming tendency, one that is particularly pronounced in central and western Türkiye. This spatial pattern is consistent with the country’s position at the intersection of several vulnerable climate zones. Türkiye sits at the eastern edge of the Mediterranean basin, a region that climate model projections have long identified as a hotspot where warming is expected to outpace the global average, and it simultaneously experiences continental influences from the Anatolian interior and maritime influences from the Black Sea, the Aegean and the Mediterranean. The new observational analysis confirms that the warming signal is not confined to one corner of the country but is a genuinely national phenomenon, with its strongest expression in the interior and western provinces.

The second robust signal in the record is a decline in wind speed, a finding that echoes what atmospheric scientists have called global terrestrial stilling. Studies published over the past fifteen years have documented widespread decreases in near-surface wind speeds across many continents, with proposed explanations ranging from increases in surface roughness driven by vegetation growth and land-use change to shifts in large-scale atmospheric circulation. The Turkish data add another national record to that growing body of evidence. For a country that has invested heavily in wind power capacity along its Aegean and Marmara coasts, the long-term behavior of surface winds is not an academic curiosity; it bears directly on the reliability of one of the fastest-growing segments of the national energy system. The study’s finding of an overall decline in wind speed across the 1946 to 2025 period will therefore be scrutinized closely by both climatologists and energy planners.

Not every variable in the analysis tells such a decisive story. Sea-level pressure and precipitation both exhibit trends that are weaker and far more regionally variable than those for temperature and wind. This is in many ways exactly what one would expect. Precipitation over Türkiye is governed by a complicated interplay of Mediterranean storm tracks, Black Sea moisture fluxes, orographic lifting over the North Anatolian mountains and the Taurus range, and the seasonal migration of the subtropical high. Averaging such a heterogeneous set of processes into a single national trend risks obscuring the local signals, and the study’s authors appear to have taken that heterogeneity seriously by reporting the regional variability rather than forcing a single narrative onto the rainfall data.

One of the study’s most technically interesting results concerns the relationships among the surface variables themselves. When the researchers computed correlations between temperature, pressure, wind and precipitation anomalies, they found that the variables are generally only weakly correlated with one another. This weak internal coupling is a meaningful diagnostic. It indicates that each variable responds to a different combination of regional atmospheric circulation patterns and local geographical controls, such as elevation, distance from the sea and topographic exposure. In a strongly coupled system, a change in circulation would move temperature, pressure, wind and rainfall together in predictable ways; in the Turkish record, each variable appears to march to its own drummer to a considerable degree, which complicates any attempt to reconstruct one variable from another or to attribute all observed changes to a single driver.

The heart of the paper, however, lies in its search for teleconnections, the statistical fingerprints that distant oscillations leave on regional climate. The authors examined three candidates. The first, the Quasi-Biennial Oscillation, is a stratospheric phenomenon in which equatorial winds alternate between easterly and westerly phases on a cycle of roughly 28 months, descending through the stratosphere from about 10 hPa down to about 70 hPa and beyond. The QBO is known to modulate the polar vortex, the Madden-Julian Oscillation and, in some studies, monsoon systems, so a link to mid-latitude surface climate is physically plausible. Yet when Ansari and Kayıkçı tested the QBO at both the 10 hPa and 70 hPa levels against their Turkish surface records, they found no statistically significant relationship with any of the four climate variables. For Türkiye, at least, the stratospheric biennial pulse appears to be a non-player at the surface.

The story changes when the analysis turns to the North Atlantic Oscillation and the Arctic Oscillation. The NAO describes a seesaw in atmospheric mass between the Azores high and the Icelandic low, while the AO captures the annular mode of the Northern Hemisphere, a ring of alternating pressure between the Arctic and the mid-latitudes. Both are dominant modes of extratropical variability, and both steer storm tracks and air masses across Europe and the eastern Mediterranean. The annual correlation analysis revealed a significant positive relationship between the NAO and precipitation, and between the AO and sea-level pressure, over Türkiye. In other words, when the NAO is in a positive phase, Turkish precipitation tends to be higher in the annual mean, and the AO leaves a clear positive signature in the country’s surface pressure record. These are exactly the kinds of relationships one would anticipate from the physics of mid-latitude circulation, in which positive NAO phases strengthen westerly flow and shift storm activity in ways that alter moisture delivery to Anatolia.

To quantify how much of the Turkish climate variability these circulation modes can actually explain, the researchers built combined regression models using the NAO and AO indices together as predictors. For sea-level pressure, the combined NAO-AO model explained 8.0 percent of the variance, a result the authors report as statistically significant. Eight percent may sound modest, and in absolute terms it is, but for annual mean surface pressure over a region as topographically and climatologically complex as Türkiye, a statistically robust eight percent is a genuine signal. It confirms that the mid-latitude annular modes are relevant players in Turkish climate variability, even if they are far from the whole story. By contrast, the corresponding regression models for temperature, wind speed and precipitation were weak or only marginally significant, underscoring that local and regional factors, along with the broader warming trend, dominate those variables more than any single teleconnection index can.

The broader significance of the study lies in its careful separation of what matters and what does not for Turkish climate variability. Over an 80-year window, the dominant signals are a clear warming trend concentrated in the central and western regions, a widespread stilling of near-surface winds, and a statistically detectable but partial influence of the NAO and AO on pressure and rainfall. The QBO, despite its importance in stratospheric dynamics and its documented teleconnections elsewhere in the world, drops out entirely at the surface over Türkiye. For climate modelers working on the eastern Mediterranean, these results provide a benchmark for evaluating which modes of variability regional simulations must capture, and for water managers, energy planners and agricultural stakeholders in Türkiye, the message is that the country’s climate future will be shaped less by exotic stratospheric oscillations than by the relentless background warming and the shifting behavior of the Atlantic-centered circulation patterns on its doorstep.

Subject of Research: Long-term climate variability and teleconnection patterns over Türkiye

Article Title: Long-term climate anomalies in Türkiye and their relationships with the QBO, NAO and AO

Article References: Ansari, K., & Kayıkçı, E. T. (2026). Long-term climate anomalies in Türkiye and their relationships with the QBO, NAO and AO. Theoretical and Applied Climatology, 157(10), Article 613. https://doi.org/10.1007/s00704-026-06541-z

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06541-z

Keywords: Türkiye, climate change, temperature trends, wind speed, precipitation, North Atlantic Oscillation, Arctic Oscillation, Quasi-Biennial Oscillation, teleconnections, sea-level pressure, GSOD stations, Theoretical and Applied Climatology

The core of the approach is a machine learning technique called a self-organizing map, or SOM, an unsupervised neural network first introduced by Teuvo Kohonen in the 1980s. SOMs have long been a workhorse of synoptic climatology, the branch of meteorology that classifies daily weather into recurring circulation patterns. Instead of asking whether a rainfall gauge has crossed some magic number, the method asks a more fundamental question: which large-scale atmospheric pattern is sitting over the region today? By grouping thousands of daily weather maps into a grid of representative patterns, the SOM turns the messy continuum of atmospheric states into an ordered catalogue of circulation regimes.

Islam and Sheridan trained separate 9 by 9 SOMs—one for each of five near-surface atmospheric variables drawn from the NCEP/NCAR Reanalysis, a widely used gridded reconstruction of historical weather. The five variables were sea-level pressure, 2-meter air temperature, 2-meter relative humidity, near-surface winds, and precipitation. Each of the 81 nodes in a given SOM represents a distinct, characteristic pattern of that variable across the South Asian domain. Every day in the reanalysis record is then mapped to the node whose pattern it most closely resembles, effectively assigning each day a circulation fingerprint.

The crucial innovation comes next. Rather than eyeballing the map grid to decide which patterns belong to which season, the researchers objectively assigned nodes to the four meteorological seasons recognized in the region—winter, pre-monsoon, monsoon, and post-monsoon. Once every node carries a seasonal label, the transition dates fall out naturally: the monsoon begins on the day the atmosphere settles into monsoon-labeled patterns and ends when it departs them. Because the assignment is systematic, the same procedure can be repeated for any variable, any region, or any sub-period, producing onset and withdrawal dates that are reproducible rather than dependent on the judgment of individual analysts.

The results reveal a striking and previously underappreciated picture of how differently the seasons announce themselves depending on what you measure. The study found no single, distinct correlation between the onset timings derived from the five variables, meaning that pressure, temperature, humidity, winds, and rainfall each tell their own story about when the seasons turn. Monsoon season, when defined by sea-level pressure, relative humidity, or temperature, tends to run longer than when it is defined by precipitation alone—a sign that the large-scale circulation reorganizes well before the rains become sustained.

That gap is not a curiosity; it is physically meaningful. The analysis shows that precipitation-based monsoon onset arrives roughly one week later than circulation-based onset, and precipitation-based withdrawal comes roughly one week earlier than the circulation suggests. In other words, the atmosphere shifts into its summer monsoon configuration before the rain fully commits, and it begins to relax out of that configuration before the rain fully quits. This is consistent with the well-established requirement for preconditioning: the large-scale flow must build up moisture convergence and low-level winds before sustained rainfall can develop, and it lingers in a summer-like state as the rains taper off.

The framework also captured a long-term trend in the winter season that will interest climate monitors. Winter length has increased over the record, driven by onset shifting earlier and withdrawal shifting later. Because the method is threshold-free, such trends emerge directly from the circulation record rather than from any chosen definition of what counts as a winter day, making them easier to compare across studies and across variables. The authors note that the same procedure can be applied regionally as well as across the whole domain, and their results demonstrate transferability across sub-regions within the same climate regime—meaning a forecaster in Bangladesh and one in northwestern India can apply the identical recipe and get internally consistent answers.

The technical elegance of the SOM approach lies in what it removes. Traditional monsoon onset definitions—such as the classic criteria for onset over Kerala used by the India Meteorological Department—typically hinge on thresholds: so many millimeters of rain at so many stations for so many consecutive days, or a specified reversal of the upper-level zonal winds. Those criteria are operationally useful but inherently arbitrary in their cutoffs, and different thresholds yield different dates. Hydrological definitions based on vertically integrated moisture transport, dynamical definitions based on wind reversals, and station-based rainfall definitions have all coexisted for decades, each defensible yet none mutually comparable. By anchoring the seasonal transition in the identity of the circulation pattern itself, the SOM framework sidesteps the threshold problem entirely and produces what the authors describe as a robust platform for multi-variable, circulation-informed seasonal dates.

Why does this matter beyond the academic literature? Monsoon onset timing is among the most consequential pieces of seasonal information in the world. An early or late onset can shift sowing windows for rice, wheat, and cotton, alter reservoir management decisions, and modulate the risk of flooding during the pre-monsoon and post-monsoon transition periods. Forecast systems have made real progress in predicting onset—the study’s reference list includes work on coupled seasonal forecast models and extended-range prediction built on SOMs—but verification of those forecasts still depends on having a consistent, objective definition of what onset actually is. A framework that yields the same answer whether applied to pressure, humidity, temperature, winds, or rainfall, and at both continental and regional scales, gives forecasters and agrometeorologists a common yardstick for the first time.

The study also carries a broader lesson for climate science: synoptic climatology, the tradition of classifying weather maps into regimes, remains a powerful and underused lens for diagnosing seasonal transitions, not just daily weather. By demonstrating that a single unsupervised learning method can harmonize five different atmospheric variables into one coherent seasonal calendar, Islam and Sheridan have offered the monsoon research community something it has long lacked—a uniform, reproducible, and threshold-free declaration of when the seasons begin and end. As the climate continues to warm and the stakes of getting the monsoon calendar right continue to rise, frameworks like this one may become the standard against which both observations and forecasts are judged.

Subject of Research: A threshold-free self-organizing map framework for determining uniform seasonal onset and withdrawal dates in the South Asian monsoon

Article Title: Uniform seasonal onset declaration in the South Asian monsoon: a SOM-based circulation framework

Article References: Islam, M. R., & Sheridan, S. C. (2026). Uniform seasonal onset declaration in the South Asian monsoon: a SOM-based circulation framework. Theoretical and Applied Climatology, 157(10), Article 660. https://doi.org/10.1007/s00704-026-06598-w

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06598-w

Keywords: South Asian monsoon, monsoon onset, self-organizing maps, synoptic climatology, NCEP/NCAR Reanalysis, seasonal transitions, machine learning, precipitation, sea-level pressure, climate variability, monsoon withdrawal, meteorology

The research examined the summers of 1980 through 2025, a 46-year window long enough to capture both year-to-year variability and slower structural change. Diem applied principal component analysis to sea-level pressure fields from the ERA5 global reanalysis, a state-of-the-art dataset produced by the European Centre for Medium-Range Weather Forecasts that blends millions of observations with a physical model of the atmosphere. Principal component analysis is a statistical technique that compresses many correlated variables, in this case pressure values across a grid of locations, into a small number of independent patterns that explain most of the variance. The result is a set of circulation modes that describe how the subtropical high strengthens, weakens, and shifts from one summer to the next.

What makes this study distinctive is its treatment of the high’s western edge. Earlier work often summarized the WNASH with a single metric, such as the longitude of its westernmost ridge or its overall intensity averaged across the basin. But the anticyclone is not a smooth, symmetrical blob. It frequently breaks into multiple ridges, with separate lobes of high pressure over the western Atlantic and the Gulf of Mexico. Diem constructed indices specifically designed to track the positions and separation of these western ridges, then tested how well each representation of the circulation explained precipitation recorded at 1,747 rain gauges scattered across the eastern and central United States.

The answer was unambiguous: the structure of the western ridges matters more than the coarse basin-wide picture. Distinct modes of WNASH and western-ridge variability produced contrasting, regionally heterogeneous precipitation responses, with the strongest and most spatially coherent relationships appearing across the Southeast and portions of the Atlantic seaboard. This makes physical sense. The western flank of the subtropical high is where the clockwise circulation around the anticyclone funnels warm, moist Atlantic and Gulf air toward the continent, feeding the thunderstorm complexes that deliver much of the region’s summer rain. When the ridge positions shift, the moisture conveyor shifts with them, and the gauges record the consequences.

The mechanics of that conveyor are well established in the literature. The Great Plains low-level jet, a river of air a few hundred meters above the ground that surges northward at night, draws its strength partly from the pressure gradient established by the Atlantic anticyclone. Mesoscale convective systems, the organized clusters of thunderstorms that can span several states, depend on that moisture supply. Sea-breeze circulations along the Carolina and Florida coasts, which can trigger afternoon downpours, are also modulated by the position of the high. By reshaping all of these processes, the western ridges of the WNASH act as a kind of hidden thermostat on summertime hydroclimate, one that conventional indices had blurred into the background.

Perhaps the most striking finding is that the high itself has been reorganizing. Over the 1980 to 2025 period, Diem documented a structural change characterized by decreasing separation between the western Atlantic and Gulf of Mexico ridge positions. In other words, the two lobes of the anticyclone have been drifting closer together, altering the geometry of the moisture pathways into the continent. This kind of reorganization is exactly the sort of response climate scientists expect from a warming world, since subtropical anticyclones respond to greenhouse forcing and sea-surface warming in ways that can differ between ocean basins. The study’s trend analyses and comparisons between different epochs of the record allowed these slow structural shifts to be separated from ordinary year-to-year noise.

Crucially, the western-ridge indices outperformed the broader circulation modes when it came to explaining recent precipitation increases across portions of the Southeast. That is a practical result with real forecasting implications. If the geometry of the ridges tracks summer rainfall better than traditional measures, then seasonal outlooks and climate model diagnostics could be sharpened simply by changing how the circulation is described. It also refines the interpretation of earlier studies that linked southeastern rainfall variability to the high’s westward displacement, suggesting that those relationships were real but incompletely characterized, because a single ridge position cannot capture the behavior of a multi-lobed anticyclone.

Yet the study delivers an equally important caution. Substantial residual precipitation increases across the Midwest and Northeast remained unexplained after accounting for WNASH variability, indicating that recent hydroclimatic change across the eastern and central United States cannot be attributed to the Atlantic high alone. The Midwest’s summer rainfall is heavily influenced by soil-moisture feedbacks, convective systems rooted in local land-atmosphere coupling, and moisture streaming in from the Gulf of Mexico and beyond, processes only partially governed by Atlantic circulation. A warming atmosphere also holds more water vapor, roughly seven percent more per degree Celsius, which intensifies heavy rainfall regardless of where the high pressure sits. Disentangling the circulation contribution from the thermodynamic one is one of the central challenges of modern hydroclimate science, and this study draws a clearer boundary around what the WNASH can and cannot explain.

The methodological approach deserves attention as well. Combining rotated principal component analysis of pressure fields, which isolates physically meaningful circulation patterns, with targeted ridge indices and a dense network of ground-based gauges gives the analysis both dynamical rigor and observational grounding. Multiple linear regression modeling allowed the contributions of different circulation modes to be weighed against one another, while inter-epochal comparisons revealed how those relationships have themselves evolved over the decades. The precipitation data used in the study are publicly archived, and the reanalysis products are freely available, making the work reproducible and extensible by other research groups.

The broader significance extends beyond the United States. Subtropical anticyclones shape the climates of every continent bordering the great ocean basins, from the Azores High over Europe to the systems governing the Mediterranean, China, and the West African monsoon. Understanding how their internal structure, not just their average strength, controls regional rainfall is a step toward better projections of where water will be plentiful and where it will become scarce as the planet warms. For the tens of millions of Americans living from the Gulf Coast to the Great Lakes, the message of this research is that the summer rain on their roofs is choreographed, in large part, by the shifting geometry of a high-pressure dome a thousand kilometers out at sea, and that learning to read that geometry more precisely may be one of the keys to anticipating the water cycle of the coming decades.

Subject of Research: Influence of western North Atlantic subtropical high variability on summer precipitation in the eastern and central United States

Article Title: The western North Atlantic subtropical high and summer precipitation across the eastern and central United States

Article References: Diem, J. E. (2026). The western North Atlantic subtropical high and summer precipitation across the eastern and central United States. Climate Dynamics, 64(10), Article 436. https://doi.org/10.1007/s00382-026-08400-z

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08400-z

Keywords: North Atlantic subtropical high, summer precipitation, climate variability, ERA5 reanalysis, sea-level pressure, southeastern United States, Great Plains low-level jet, hydroclimate, Climate Dynamics, atmospheric circulation, precipitation trends, western ridge