The team quantified precipitation seasonality with a deliberately simple but powerful metric: for every year and every grid cell, they computed running six-month precipitation totals and identified the consecutive wettest and driest six-month windows. The difference between those two accumulations defines a seasonality index, or SI. Unlike fixed calendar-season approaches, this moving-window framework lets the timing of the wet and dry phases shift from year to year and from place to place, which matters in a region where monsoon onset and withdrawal dates vary considerably. A larger SI means a sharper contrast between the rainy and rainless halves of the annual cycle, and it is this contrast, rather than annual totals alone, that governs when water is actually available to farms, forests, and reservoirs.

Three observation-based products told a consistent story. The gauge-based Climate Prediction Center dataset recorded a seasonality increase of 8.92 percent per decade between 1981 and 2020, the satellite-gauge blended CHIRPS product showed 3.30 percent per decade, and the merged satellite-gauge GPCP dataset showed 4.24 percent per decade, all statistically significant. The ERA5 atmospheric reanalysis also produced a positive trend, a striking 20.40 percent per decade, but with such wide uncertainty that the authors treat it as a supplementary reference rather than confirmatory evidence. For context, a recent global analysis found that precipitation seasonality over global land areas has been increasing at roughly 3.1 to 3.85 percent per decade, which means the Indochina Peninsula is amplifying at least as fast as, and in some datasets considerably faster than, the planet as a whole.

Crucially, the study dissects where in the annual cycle the amplification comes from. Because the seasonality index is simply the difference between wet-season and dry-season accumulation, either component could in principle drive the trend. The observations point firmly to the wet side. In the CPC dataset, wet-season precipitation rose by 25.37 millimeters per decade while dry-season precipitation was essentially flat at 0.26 millimeters per decade. CHIRPS showed a 17.00 millimeter per decade wet-season increase alongside a 4.93 millimeter per decade dry-season decline, which compounds the contrast in the same direction. GPCP and ERA5 showed weaker and partly offsetting component trends, but across all datasets the dominant signal was the same: the monsoon half of the year is getting rainier, while the dry half is changing comparatively little.

That asymmetry has a plausible physical explanation rooted in thermodynamics. According to the Clausius-Clapeyron relationship, the atmosphere’s moisture-holding capacity rises by roughly seven percent for every degree Celsius of warming. In a monsoon climate, that extra moisture is most consequential when convection is already active, so the wet season absorbs most of the added atmospheric water vapor while the dry season, limited by circulation rather than by moisture supply, sees little benefit. The authors are careful to note that their study does not explicitly diagnose circulation changes or moisture transport pathways, and they flag monsoon dynamics, land-sea thermal contrast, and moisture transport as targets for future mechanistic work. But the thermodynamic argument aligns neatly with what both observations and models show.

To move from correlation to attribution, the team turned to the Detection and Attribution Model Intercomparison Project, part of the Coupled Model Intercomparison Project Phase 6. Five models, ACCESS-CM2, ACCESS-ESM1-5, CanESM5, MIROC6, and MRI-ESM2-0, provided parallel simulations in which the historical period is driven either by all external forcings combined, by greenhouse gases alone, by anthropogenic aerosols alone, or by natural forcings such as solar variability and volcanic eruptions alone. Trends in each experiment were estimated with the Theil-Sen slope estimator, a robust regression technique that limits the influence of anomalous years, and then averaged with equal weights across the models.

The results were strikingly directional. Under greenhouse-gas-only forcing, the multi-model mean seasonality index climbed by 15.32 millimeters per decade, driven almost entirely by a 15.96 millimeter per decade increase in wet-season precipitation, and four of the five individual models agreed on the positive sign. Under aerosol-only forcing, the picture inverted completely: all five models produced negative seasonality trends, with a multi-model mean of minus 16.07 millimeters per decade, as wet-season precipitation fell by 17.22 millimeters per decade. Natural forcing produced negative trends in four of five models, averaging minus 8.89 millimeters per decade. The all-forcing experiment landed in between, at plus 12.13 millimeters per decade, consistent with aerosols partially offsetting the greenhouse-gas-driven amplification. Spatially, greenhouse-gas forcing produced coherent positive seasonality trends across nearly the entire peninsula, while aerosol forcing produced widespread negative trends, and the all-forcing response was comparatively weak and mixed, exactly what one expects when opposing forcings compete.

The authors stress that the magnitude of the simulated responses varies substantially among models, and that only one realization per model and forcing experiment was used, so some of the spread reflects internal climate variability rather than structural differences alone. They also acknowledge that the five models were selected because they all offer the required single-forcing experiments over a common period, not because they were pre-screened for regional precipitation skill, although a prior evaluation of 35 CMIP6 models against the APHRODITE observational dataset found that most capture the major features of mainland Southeast Asian rainfall climatology. Sensitivity tests shifting the analysis start year to 1982 or 1983 left the sign and relative ordering of the forcing responses unchanged, suggesting the conclusions are not artifacts of a particular window.

The stakes for the region are considerable. The Indochina Peninsula hosts the Mekong, Irrawaddy, Salween, and Chao Phraya river systems, all of which are paced by monsoon rainfall, and its densely populated agricultural economies depend on the seasonal timing of water availability. A wet season that concentrates more rain raises flood risk in basins like the Mekong, while a dry season that shows little change in precipitation, combined with rising atmospheric evaporative demand under warming, points toward intensified seasonal water stress. Because the observed amplification is driven by wetter wet seasons rather than drier dry seasons, adaptation strategies may need to focus less on drought preparedness alone and more on managing heavier monsoon-season runoff, reservoir operation, and flood defenses.

The study also highlights a broader lesson for climate attribution science. Annual precipitation totals and even precipitation extremes, the metrics that dominate most climate assessments, can mask profound reorganizations of when rain falls within the year. Here, modest changes in annual totals coexist with a strengthening seasonal contrast that reshapes water availability, agriculture, and hazard profiles. As global analyses continue to identify anthropogenic fingerprints on the annual precipitation cycle, this regional study demonstrates that monsoon domains can amplify the global signal, and that separating the competing influences of greenhouse gases, aerosols, and natural variability is essential for projecting how the world’s most seasonally dependent societies will experience a warming climate.

Subject of Research: Attribution of observed changes in precipitation seasonality over the Indochina Peninsula to anthropogenic greenhouse gas forcing using observations and CMIP6 single-forcing simulations

Article Title: Changes in precipitation seasonality over the Indochina Peninsula during 1981–2020: observational evidence and attribution of anthropogenic forcing

Article References: Zhang, Y., Yang, K., & Du, S. (2026). Changes in precipitation seasonality over the Indochina Peninsula during 1981–2020: observational evidence and attribution of anthropogenic forcing. Frontiers in Earth Science, 14, Article 1937024. https://doi.org/10.3389/feart.2026.1937024

Image Credits: AI Generated

DOI: 10.3389/feart.2026.1937024

Keywords: precipitation seasonality, Indochina Peninsula, monsoon climate, CMIP6, DAMIP, greenhouse gas forcing, aerosol forcing, wet-season precipitation, Mekong River basin, climate attribution, Clausius-Clapeyron, water resources

The research, led by Nicolas Decoopman of the University of Grenoble Alpes together with Juliette Blanchet, Antoine Blanc, and Cécile Caillaud, evaluated the convection-permitting regional climate model AROME, developed at the French national meteorological research centre CNRM and operated by Météo-France. Unlike conventional climate models that approximate thunderstorms with statistical formulas, AROME resolves deep convection explicitly at a grid spacing of just 2.5 kilometers. The team assessed a remarkable 63-year simulation spanning 1959 to 2022, driven at its boundaries by the ERA5 global reanalysis, against thousands of rain gauge records from Météo-France stations across metropolitan France.

The scientific stakes are high because extreme precipitation behaves very differently from ordinary rain. The Clausius–Clapeyron relationship dictates that warmer air holds roughly 7 percent more moisture per degree Celsius of warming. Yet while global mean precipitation rises by only about 1 to 3 percent per degree, constrained by radiative and dynamic limits, intense convective events can condense nearly all of that surplus moisture, pushing short-duration extreme rainfall up by 5 to 8 percent per degree. In other words, the fiercest storms scale almost perfectly with the theoretical moisture ceiling, which is why a warming climate loads the dice for flash floods even when average rainfall barely changes.

France offers a compelling natural laboratory. The country has warmed by 1.7 degrees Celsius since the pre-industrial era, and 2 degrees in the Alps, and it has experienced a string of spectacular recent events: intense high-altitude rainfall that flooded the Écrins massif in June 2024, more than 600 millimeters of rain in 48 hours on the western slopes of the Massif Central that October, and violent thunderstorms in May 2025 with locally more than 120 millimeters per hour. Previous studies have shown that daily precipitation extremes in southeastern France have intensified by roughly 20 percent since the early 1990s, with estimates reaching up to 40 percent, and that in the southwestern Alps autumn 20-year return levels climbed by as much as 100 percent between 1958 and 2017.

To evaluate the model, the researchers applied the machinery of extreme value theory, fitting generalized extreme value distributions to annual maxima of daily and hourly precipitation. The 10-year return level, the rainfall amount exceeded on average once per decade, served as the primary metric. Crucially, the team went beyond a static climatology by employing non-stationary GEV models in which the location and scale parameters evolve linearly in time, including breakpoint variants that allow trends to begin in 1985, the period when French extreme-precipitation signals are thought to have emerged. The analysis drew on 1,583 daily stations with at least 50 years of quality-controlled data and 574 hourly stations with at least 25 years, using the hydrological year running from September to August so that autumn Mediterranean episodes are not artificially split across two calendar years.

At the daily scale, the verdict was largely favorable. AROME reproduced the large-scale spatial structure of French precipitation climatology with impressive fidelity: Pearson correlations of 0.95 for the annual frequency of wet days, 0.94 for annual totals, and 0.95 for the daily 10-year return level. The model correctly captured the aridity of the Rhône valley and Mediterranean coast, where fewer than 70 wet days per year are recorded, in contrast to the Alps, Pyrenees, and Massif Central, which receive more than 1,800 millimeters annually across 140 to 160 wet days. Mean biases were remarkably small, just 11 millimeters per year for annual totals and minus 2 millimeters for the daily return level, although structured regional errors appeared, including excesses of more than 300 millimeters per year along the Pyrenean ridge and deficits of up to 400 millimeters stretching from the northern Pre-Alps to the Vosges.

The trend analysis at the daily scale confirmed known patterns and revealed the model’s strengths and weaknesses. Observations show coherent increases of 5 to more than 30 percent in the 10-year return level along the Rhône Valley and in the southern Alps, a diagonal band of decrease from the Mediterranean to the Atlantic coast in winter, widespread positive spring trends locally exceeding 35 percent, and predominantly negative summer trends across the southern half of the country. AROME reproduced the broad spatial organization of these patterns, performing best in autumn with a correlation of 0.40, but it systematically underestimated trend amplitudes. Part of that dampening has a clear explanation: the model’s simulated warming over France is about one-third weaker than observed, which weakens the Clausius–Clapeyron-driven component of extreme rainfall trends, likely due to the model’s internal sensitivity to radiative forcing and its static land-use map.

The hourly results tell a more troubling story, and they constitute the first nationwide mapping of hourly extreme-precipitation trends in France. Station data over 1990 to 2022 show markedly noisier and more extreme signals than at the daily scale, with significant trends ranging from minus 50 percent to beyond plus 100 percent, concentrated in convective windows: monthly median trends reached 101.86 percent in June, 53.88 percent in October, and 43.28 percent in February. AROME captured the positive sign of these monthly peaks but drastically underestimated their magnitude, simulating a median June trend of just 17.70 percent, and its spatial correlation with observed hourly trends collapsed to nearly zero during convective months, 0.08 in June and 0.04 in March. The model also underestimated hourly 10-year return levels by 23.30 percent on average, with widespread deficits of 5 to 20 millimeters, and the authors note that gauge undercatch in windy conditions means the true underestimation is probably even larger.

Several factors conspire to make hourly extremes so difficult. A single convective core may occupy only part of a 2.5-kilometer grid cell, so a station can record a downpour far more intense than the grid-box average even when the storm is realistically simulated. The sparse hourly network, which is notably thin in the Alps and Pyrenees, further limits evaluation, and the short 33-year hourly record means the climate signal may still be emerging from year-to-year noise. Earlier work suggests that running the model at 1.3 kilometers improves the representation of peak intensities, and the authors found that correlations rise by about 30 percent for 6- and 9-hour accumulations, hinting that AROME may spread convective rain too widely in space and time, diluting local extremes.

The study’s conclusions carry real weight for how France prepares for a wetter, stormier future. The 63-year AROME simulation, one of the longest convection-permitting hindcasts ever produced, is demonstrably valuable for studying daily extremes and can support national-scale climate analyses, provided trend magnitudes are interpreted cautiously. But for the short-duration convective deluges that trigger flash floods, the model’s hourly trends are, in the authors’ words, almost nonexistent, and future evaluations should turn to high-resolution radar-gauge reanalyses such as Météo-France’s 1-kilometer COMEPHORE product. As kilometer-scale climate models edge toward operational use in projections, this work draws a clear line: they excel at the storms we can watch unfold over days, but the hour-long cloudbursts that matter most for human safety still test the limits of what even the finest simulation grids can resolve.

Subject of Research: Evaluation of a convection-permitting regional climate model's simulation of extreme precipitation trends in France

Article Title: Climatology and trends of extreme precipitation in France: evaluation of an explicit-convection regional climate model

Article References: Decoopman, N., Blanchet, J., Blanc, A., & Caillaud, C. (2026). Climatology and trends of extreme precipitation in France: evaluation of an explicit-convection regional climate model. Hydrology and Earth System Sciences, 30(18), 5833-5855. https://doi.org/10.5194/hess-30-5833-2026

Image Credits: AI Generated

DOI: 10.5194/hess-30-5833-2026

Keywords: extreme precipitation, convection-permitting model, AROME, France, flash floods, extreme value theory, climate change, Clausius–Clapeyron, hourly rainfall, regional climate modeling, ERA5 reanalysis, Météo-France

The team, led by Rhidian Thomas of the National Centre for Atmospheric Science and the University of Reading, together with colleagues from the University of Edinburgh, the University of Reading, and NOAA’s Physical Sciences Laboratory, used a sophisticated technique built on the NOAA-CIRES-DOE Twentieth Century Reanalysis version 3, known as 20CRv3. A reanalysis system combines a numerical weather model with historical observations to reconstruct the atmospheric state of the past. In this case, the system assimilated more than a century-old archive of surface pressure readings from land stations, ships, and tropical cyclone reports, using an Ensemble Kalman Filter to blend those observations with the NOAA GFS atmospheric model. The result is an 80-member ensemble of three-hourly atmospheric fields covering all of 1903, a year that ranks as the third coldest in the 175-year HadCRUT5 temperature record, partly because of the cooling influence of the Santa Maria volcanic eruption of 1902.

The crucial innovation lies in what the researchers did next. They re-ran the entire data assimilation system, feeding it exactly the same pressure observations but with two deliberate changes: sea surface temperatures were uniformly raised by 2 degrees Celsius, and in a second, novel experiment, atmospheric carbon dioxide was lifted to 530 parts per million on top of the ocean warming. Because the same pressure data constrain the atmospheric flow in every experiment, all three simulations experience essentially identical weather patterns, the same storms tracking the same paths, the same anticyclones parking over the same regions. Any difference between the runs therefore reflects the pure thermodynamic response to a warmer, moister atmosphere, isolated from the uncertain changes in large-scale circulation that dominate disagreements between climate models.

This storyline approach, related to pseudo-global-warming and nudging methods used in event attribution, has a powerful advantage: it sidesteps the largest source of uncertainty in regional climate projections. Regional forecasts of temperature and rainfall depend heavily on how storm tracks, jet streams, and blocking patterns might shift, questions on which models diverge. By anchoring the analysis in weather patterns that demonstrably occurred, the reanalysis experiments answer a cleaner question: if the atmosphere’s circulation stayed the same, how would the same weather feel in a warmer world? The team focused on four regions where 1903’s pressure observations were dense enough to tightly constrain the circulation: northwestern Europe, the western Mediterranean, the East Coast of the United States, and southeastern Australia.

The temperature results are unambiguous. In the warmer experiments, 99 percent of days became warmer than in the original reconstruction, with typical days over land warming by roughly 2 to 2.5 degrees Celsius for a 2-degree ocean warming. But the warming was far from uniform across the temperature distribution. The largest increases, 4 to 5 degrees Celsius, fell on the coldest days, those dipping below freezing. The mechanism is a classic snow-albedo feedback: in the warmer simulations, less of the land surface is covered in snow, so proportionally more sunlight is absorbed by darker ground rather than reflected back to space, amplifying warming precisely on the days when snow would otherwise lie. Southeastern Australia, where freezing days are rare, was the lone region without this cold-day amplification.

Hot days warmed disproportionately too, with increases of 3 degrees Celsius or more on days above 30 degrees in the western Mediterranean, the US East Coast, and southeastern Australia. The underlying physics varies by region. On the US East Coast, the hottest days coincide with dry soils, and the simulations show a sharp reduction in evaporative cooling above the 97th temperature percentile, meaning the land surface loses its ability to shed heat through evaporation, a well-known mechanism that intensifies heatwaves. In southeastern Australia, by contrast, the amplification appears tied to reduced cloud cover and greater incoming solar radiation. Notably, the direct radiative effect of the added carbon dioxide contributed an extra 0.2 to 0.4 degrees of warming on typical days, and as much as a fifth of the total warming over the arid western United States, demonstrating that carbon dioxide warms the surface directly, not merely indirectly through ocean warming.

Rainfall told an equally counterintuitive story. Globally, total precipitation rose in the warmer experiments, but far more slowly than atmospheric moisture, which climbed at roughly 8.5 percent per degree of warming, in line with the Clausius-Clapeyron relation governing how much water vapor air can hold. The reason is the atmosphere’s energy budget: latent heating from rain must balance longwave cooling, and additional carbon dioxide suppresses longwave emission to space, blunting the precipitation increase. Regionally, the pattern echoed canonical model projections, with wetting across much of the extratropics and drying over the Mediterranean and subtropical oceans. Even in the western Mediterranean, where annual rainfall declined, daily rainfall variability increased, meaning larger swings from wet day to dry day, a signature consistent with emerging observational trends.

The most vivid finding concerns how the extra rain is delivered. Across all four regions, the number of days with light, drizzly rain decreased, while days with moderate to heavy rain grew exponentially more frequent as rain rates increased. The cutoff scale separating ordinary from heavy events shifted upward roughly in step with Clausius-Clapeyron scaling, yet the time-mean increase in rainfall was concentrated on fewer than 1 day in 10. In other words, even where total annual rainfall rises, most wet days actually become drier, and the annual surplus arrives in a handful of intense bursts. Dry days also increased in three of the four regions, likely because reduced relative humidity over land raises convective inhibition, lengthening the gaps between rain events.

October 1903 provides the study’s showpiece. That month remains the wettest calendar month on record for the United Kingdom, with 220 millimeters in the national series back to 1836. The reanalysis reproduced the observed geography of the deluge, and in the warmer experiments, rainfall over the UK and Ireland rose by 12 to 17 percent, equivalent to 6 to 8.8 percent per degree of local warming, well above the annual-mean scaling. On sub-daily timescales the amplification was even more dramatic: one intense three-hour event on 6 October intensified by 31 percent, around 15 percent per degree, exceeding Clausius-Clapeyron scaling as previous studies have found for short-duration extremes. Scaling to today’s roughly 1.5 degrees of warming since 1903, the authors estimate that the same weather pattern would now deliver around 240 millimeters across the UK, comfortably surpassing the previous record of 216.3 millimeters set in December 2015.

The study’s limitations are candidly acknowledged. The experiments are idealised, with uniform ocean warming, unchanged sea ice, and a single year of weather, and the assumptions that 1903’s patterns could occur in a warmer world, and that the assimilation remains consistent under perturbed boundary conditions, are supported but not proven; the warmer runs did reject somewhat more pressure observations, particularly over densely observed northwestern Europe. Circulation changes, if they occur, could amplify or modify these thermodynamic signals, and long-standing model biases in light rainfall temper confidence in the drizzle decline. Yet the fact that a single pair of counterfactual reanalysis runs recovers so many robust patterns seen across generations of climate models, amplified warming of hot and cold extremes, rising precipitation variability, fewer rainy days, and heavier downpours, makes this a powerful, computationally efficient new line of evidence. It suggests that the everyday weather of the past is already a preview of a different daily experience in our warming future, one written into every frost-free night and every intense burst of rain.

Subject of Research: Thermodynamic impacts of climate change on everyday temperature and precipitation, studied by reanalysis experiments translating the weather of 1903 into a warmer world

Article Title: Everyday weather in a warmer world

Article References: Thomas, R., Compo, G. P., George, S., Hegerl, G. C., Schurer, A., Shepherd, T. G., Slivinski, L. C., Thompson, V., & Hawkins, E. (2026). Everyday weather in a warmer world. Earth System Dynamics, 17(5), 1365-1380. https://doi.org/10.5194/esd-17-1365-2026

Image Credits: AI Generated

DOI: 10.5194/esd-17-1365-2026

Keywords: climate change, reanalysis, 20CRv3, extreme weather, precipitation, temperature extremes, Clausius-Clapeyron, storyline attribution, 1903, hydroclimate, snow albedo feedback, Mediterranean drying

The foundation of the work is a carefully quality-controlled dataset of daily precipitation observations from 24 meteorological stations spanning the years 2010 to 2024. Before any modeling could begin, the authors subjected these records to rigorous homogeneity testing, using classical statistical procedures such as the Alexandersson standard normal homogeneity test and the Pettitt change-point test to ensure that the trends they detected were genuine features of the climate rather than artifacts of station relocations or instrument changes. What emerged from this screening was striking: a statistically significant and spatially coherent shift in the precipitation regime centered around the year 2020, with a domain-wide mean annual precipitation anomaly of minus 20.5 percent relative to the baseline decade. In other words, the region has recently entered a markedly drier state, and the drying is not confined to isolated locations but sweeps across the entire coastal domain.

To make sense of the region’s spatial complexity, the researchers turned to unsupervised machine learning. Using K-Means clustering, an algorithm that partitions data points into groups based on their similarity, they divided the study domain into three physically interpretable subregions: Coastal, Inland, and Mountainous. The validity of this partition was assessed with silhouette analysis, a standard diagnostic that measures how well each station fits its assigned cluster compared with neighboring clusters. The resulting zones are not arbitrary statistical constructs; they correspond to real gradients in topography, moisture availability, and storm behavior, with the Taurus range acting as a formidable orographic barrier that shapes how moisture from the Mediterranean is converted into rainfall.

At the heart of the study sits an explainable machine learning classifier built on XGBoost, a gradient-boosted decision tree system that has become a workhorse of modern environmental prediction. The classifier was trained to distinguish extreme precipitation days from ordinary ones, using a rich set of candidate predictors drawn from multiple sources: ERA5-Land reanalysis data, satellite-derived products, and large-scale teleconnection indices from NOAA. Crucially, the model was validated temporally, meaning it was tested on data from periods separate from its training window, a practice that guards against the inflated skill scores that plague many machine learning applications in climate science. The classifier achieved an area under the receiver operating characteristic curve, or AUC-ROC, of 0.835, a strong diagnostic performance that indicates genuine predictive skill rather than memorization.

But the real breakthrough lies in what the model could explain. Using TreeSHAP, a game-theoretic attribution method that quantifies each variable’s contribution to every individual prediction, the authors interrogated the black box and found that near-surface relative humidity is the overwhelmingly dominant thermodynamic trigger for extreme precipitation events, with a mean absolute SHAP value of 0.557. Even more revealing was the shape of the relationship: the model identified a non-linear activation threshold near 65 to 70 percent relative humidity, below which extreme rainfall becomes unlikely regardless of other conditions. This sharp threshold is consistent with Clausius-Clapeyron constraints, the fundamental thermodynamic law dictating that warmer air can hold roughly seven percent more water vapor per degree Celsius of warming, and that heavy precipitation requires a saturated lower atmosphere to draw upon.

The SHAP analysis also uncovered a second, spatially structured insight: the humidity control on extreme precipitation intensifies with elevation. In the mountainous subregion along the Taurus range, the coupling between low-level moisture and extreme rainfall is stronger than along the coast, a signature of orographic enhancement in which moist air masses are forced upward over terrain, cool adiabatically, and release their water as intense, localized downpours. This finding matters for practical reasons as much as scientific ones, because it tells water managers and emergency planners that the same atmospheric moisture can produce very different flood outcomes depending on where it intersects the topography, and that mountain communities face a distinct and amplified exposure.

To quantify how often such extremes can be expected to recur, the authors applied Generalized Extreme Value modeling, the statistical framework that underpins modern flood and rainfall frequency analysis. Rather than relying solely on the relatively short station records, they fitted GEV distributions to a 44-year historical baseline from the CHIRPS satellite-gauge blended precipitation dataset, which provides the long record needed to constrain estimates of rare events. The results reveal a severe concentration of hazard along the eastern inland corridor of the study domain, and a domain-mean 100-year return level of 102.4 millimeters per day. That figure represents the daily rainfall total that, on average, should be exceeded only once per century, and it provides a concrete benchmark for the design capacities of dams, culverts, and urban drainage systems across the region.

The final and perhaps most consequential component of the pipeline looks forward. Drawing on the Coupled Model Intercomparison Project Phase 6, or CMIP6, the authors examined projections under two shared socio-economic pathways: SSP2-4.5, an intermediate emissions scenario, and SSP5-8.5, a high-emissions trajectory. By the period 2041 to 2060, both scenarios indicate robust declines in the frequency of extreme precipitation events, by 19.5 percent under SSP2-4.5 and 12.1 percent under SSP5-8.5. Taken together with the observed drying trend since 2020, these projections point toward a transitional shift in the region’s hydroclimate, one that the authors characterize as a regime of fewer but potentially more intense convective precipitation events.

This apparent paradox, in which total rainfall declines while the most intense downpours persist or even intensify, is one of the most actively debated features of Mediterranean climate change. The Eastern Mediterranean and Middle East have been identified as a prominent climate change hotspot, and previous research has documented how reduced large-scale circulation and diminishing overall precipitation can coexist with a thermodynamic intensification of the heaviest events, which draw on a warmer, moister boundary layer when the right synoptic triggers do arrive. The findings from coastal Türkiye fit squarely within this emerging picture, and they carry a sobering implication: infrastructure designed around historical rainfall frequencies may be simultaneously overbuilt for the new average conditions and underbuilt for the extremes that remain.

What distinguishes this study is less any single result than the architecture that connects them. By weaving together multi-source data fusion, spatial regionalization, explainable classification, extreme value analysis, and scenario-based projection, the authors demonstrate a template that other data-sparse, hazard-exposed regions could readily adopt. The explainability layer is particularly significant for the credibility of machine learning in climate applications, because it transforms the model from an opaque predictor into a scientific instrument that recovers physically meaningful relationships, such as the humidity activation threshold and its orographic amplification, that forecasters and policymakers can actually interrogate and trust. For the coastal communities of Eastern Mediterranean Türkiye, the message is twofold: the climate is already measurably drier than it was a decade ago, and the floods that do arrive will demand respect, because the atmosphere’s capacity to deliver them in concentrated bursts remains fully intact.

Subject of Research: Explainable machine learning and extreme value analysis of extreme precipitation drivers, recurrence, and CMIP6 projections in coastal Eastern Mediterranean Türkiye

Article Title: Explainable machine learning and extreme value analysis for precipitation projections in coastal Eastern Mediterranean Türkiye

Article References: Deniz Cüvitoğlu, F., & Cüvitoğlu, A. (2026). Explainable machine learning and extreme value analysis for precipitation projections in coastal Eastern Mediterranean Türkiye. Theoretical and Applied Climatology, 157(10), Article 637. https://doi.org/10.1007/s00704-026-06560-w

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06560-w

Keywords: extreme precipitation, explainable machine learning, XGBoost, TreeSHAP, Generalized Extreme Value analysis, CMIP6, SSP2-4.5, SSP5-8.5, Eastern Mediterranean, Türkiye, Clausius-Clapeyron, orographic enhancement