AI Reveals Humidity Trigger Behind Türkiye’s Shifting Extreme Rainfall Regime

Along the eastern Mediterranean coast of Türkiye, where the Taurus Mountains rise steeply from the sea and storm systems funnel into narrow coastal plains, extreme rainfall has long been one of the region’s most dangerous natural hazards. Flash floods in cities such as Mersin and Adana have repeatedly overwhelmed drainage networks, damaged infrastructure, and disrupted lives. Yet despite the severity of the threat, no single study had ever combined the physical drivers, the statistical recurrence properties, and the future trajectories of these extremes into one coherent framework for this specific subregion. A new study published in Theoretical and Applied Climatology by Fatma Deniz Cüvitoğlu of Mersin University and Ali Cüvitoğlu of Adana Alparslan Türkeş Science and Technology University closes that gap with an ambitious hybrid pipeline that fuses observational records, machine learning, extreme value statistics, and the latest generation of climate model projections.

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