De Martonne proposed one of the earliest quantitative aridity indices to characterize long-term moisture availability using annual mean temperature and total precipitation [9]. The index is defined as: (1)Idm =PT+10where

P = annual precipitation total (mm),

T = annual mean temperature (°C).

The constant +10 prevents the denominator from approaching zero or becoming negative in regions where monthly mean temperatures fall below 0°C. Although widely used due to its simplicity, the DMCC becomes less reliable when long-term datasets include extended cold seasons or strong seasonal contrasts, which may exaggerate or suppress aridity levels [12]. Moreover, the method does not incorporate any form of normalization, making cross-regional comparisons difficult when climatic amplitudes differ substantially.

Erinç introduced a precipitation efficiency index specifically suited to regions such as Türkiye, where the contrast between dry summers and wet winters is pronounced [11]. In contrast to De Martonne’s use of annual mean temperature, Erinç employed the mean of monthly maximum temperatures, thereby better reflecting the evaporative potential of warm-season conditions.

The index is expressed as: (2)Im =PTom where

P = annual total precipitation (mm),

T_om = annual mean of monthly maximum temperatures (°C).

By using monthly maximum temperatures rather than annual means, the Erinç Index provides a more realistic representation of summer aridity, which is a defining characteristic of Türkiye’s Mediterranean and continental climatic regimes. However, the index remains scale-dependent, as it relies on raw temperature (°C) and precipitation (mm) values. As a result, regions with similar precipitation but different thermal amplitudes may be classified inconsistently. For example, 700 mm of rainfall paired with T_om = 20°C yields I_m = 35, whereas 400 mm with T_om = 10°C yields I_m = 40—suggesting a more “humid” classification despite substantially lower precipitation.

Such discrepancies highlight the need for a normalized and unitless classification that can provide coherent comparisons across regions with diverse climatic characteristics. This methodological gap motivates the development of the U2 Climate Classification.

The U2 Index is a normalized and unitless climate classification method designed to overcome structural limitations inherent in DMCC and ECC.

In this study, the terms aridity, humidity, and drought are used descriptively to express long-term climatic moisture balance rather than short-term meteorological anomalies.

U2 introduces three major methodological improvements: 1.

Normalization of variables:

Both temperature and precipitation are normalized relative to their long-term means, eliminating unit dependency and reducing the influence of extreme seasonal values.

The U2 Classification therefore provides a more physically consistent representation of Türkiye’s climatic balance by integrating long-term thermal and hydrological signals into a unified, dimensionless index. Detailed explanations of the normalization procedure, index formulation, and seasonal realignment are provided in the following sections.

Long-term meteorological observations covering the period 1927–2023 were obtained from the Turkish State Meteorological Service (TSMS), including monthly mean temperature and monthly total precipitation for all 81 provinces of Türkiye. These datasets form the primary basis of the U2 Climate Classification. To ensure consistency and completeness, the long-term series were screened for missing values, temporal discontinuities, and internal inconsistencies using standard quality-control procedures. This screening was applied solely to verify the temporal stability and reliability of the TSMS time series; no external datasets or reanalysis products were incorporated into the U2 Index calculations.

Spatial representativeness was maintained by using provincial boundaries derived from Thiessen polygons, which approximate the effective spatial domain of meteorological stations. This approach provides a realistic partitioning of Türkiye’s climatic regions, especially in areas where station density varies with topography and population distribution.

For visualization and pattern interpretation, 12 representative provinces were selected: Adana, Konya, Istanbul, Kars, Rize, Antalya, Bursa, Amasya, Bitlis, Eskişehir, Şanlıurfa, and Canakkale. These provinces were chosen to reflect the full range of Türkiye’s climatic subregions—including Mediterranean, continental, maritime, high-altitude, and transitional zones—and to demonstrate the ability of the U2 Index to capture spatial climatic heterogeneity. All spatial analyses, mapping procedures, and pattern extractions were performed using normalized monthly temperature and precipitation series derived from these datasets.

Based on the distribution of normalized U2 Index values across all provinces and the comparison of these values with established thresholds from the De Martonne (DMCC) and Erinç (ECC) classifications, a set of nine climatic classes was defined for the U2 Climate Classification. These classes reflect a continuous gradient from exceptionally humid to extremely arid conditions and are designed to maintain consistency with traditional methods while taking advantage of U2’s normalized, unitless structure. The classification criteria are presented in Table 1.

These ranges were calibrated through iterative comparison with DMCC and ECC thresholds and validated against observed spatial patterns of Türkiye’s climatic zones. This approach ensures that the U2 classes retain compatibility with classical aridity assessments while providing improved sensitivity in transitional regions due to their normalized and dimensionless formulation.

To assess internal consistency and evaluate how the U2 Index aligns with established climatic indicators, U2 values were compared with outputs from the De Martonne (DMCC) and Erinç (ECC) classifications using Pearson correlation analysis. This procedure quantifies the degree to which spatial patterns produced by U2 correspond to those generated by classical aridity formulations.

The analysis revealed strong and statistically significant correlations between U2 and both DMCC and ECC across all provinces, indicating that the normalized and unitless structure of U2 preserves the fundamental climatic gradients represented by traditional indices. At the same time, spatial inspection showed that U2 provides greater detail in transitional climate zones, where DMCC and ECC tend to generalize moisture levels due to their dependence on raw temperature–precipitation ratios.

These results confirm that the U2 Index maintains physical coherence with conventional climatological approaches while offering enhanced sensitivity and spatial resolution, thereby supporting its validity as a robust classification tool for Türkiye’s diverse climatic regimes.

The U2 Climate Classification was applied using long-term temperature and precipitation datasets obtained from the Turkish State Meteorological Service (TSMS), covering the period 1927–2023 for all 81 provinces of Türkiye. The methodological workflow consists of five sequential steps: data compilation, temperature transformation, normalization, index calculation, and spatial mapping. These steps are summarized in Fig. 3.

(1) Data Compilation

For each province, monthly mean temperature (°C) and monthly total precipitation (mm) were compiled into continuous long-term series. Data screening procedures were applied to detect missing values, discontinuities, or anomalous entries, ensuring internal consistency prior to analysis.

(2) Temperature Conversion

To eliminate negative-temperature distortions in subsequent ratio-based calculations, all temperature values were first converted from Celsius to Fahrenheit using Eq. (3): T(∘F)=1.8T(∘C)+32

This transformation prevents zero or negative values from artificially inflating or suppressing normalized ratios, while preserving intermonthly temperature variability.

(3) Normalization

Normalization was performed independently for each province to create a dimensionless representation of climatic variability. Monthly temperature and precipitation values were divided by their respective long-term annual means: Tnorm=Tmonthly(F)Ty  Pnorm=PmonthlyPy

The normalized series were then used to derive the four extreme parameters (Tmax,Tmin,Pmax,Pmin) which constitute the components of the U2 Index (Eqs. (4)–(8)).

(4) U2 Index Calculation

The U2 Index for each province was calculated using Eq. (8): Iu2=Tmax−PminPmax−Tmin 

This formulation represents the relative displacement between thermal and hydrological extremes, enabling a physically meaningful comparison of climatic balance across Türkiye.

(5) Mapping and Visualization

Normalized U2 values were spatially analyzed to produce the U2 Climate Classification Map. Twelve representative provinces (Adana, Konya, Istanbul, Kars, Rize, Antalya, Bursa, Amasya, Bitlis, Eskişehir, Şanlıurfa, and Çanakkale) were selected to illustrate the diversity of climatic patterns in Türkiye and to demonstrate the applicability of the index across contrasting environments.

Spatial interpolation and classification procedures were performed in a GIS environment, producing maps that highlight regional climatic zones and transitional boundaries. The complete methodological workflow, from data acquisition to mapping, is summarized in Fig. 3.

City names and coordinates are provided in Appendix A.

The comparative performance of the De Martonne (DMCC), Erinç (ECC), and U2 Climate Classification (U2CC) methods is summarized in Table 2. The three indices exhibit distinct sensitivities to temperature, precipitation, and seasonal variability due to differences in their mathematical structures and normalization approaches.

U2CC uses temperature values converted from °C to °F during preprocessing; however, the final index is fully dimensionless.

Across Türkiye, DMCC and ECC generally reproduce the large-scale climatic divisions but tend to oversimplify conditions in semi-arid and sub-humid regions due to their reliance on raw temperature–precipitation ratios. ECC partially improves warm-season sensitivity by incorporating monthly maximum temperatures, yet both classical indices remain constrained by unit dependency and unequal rainfall distribution.

In contrast, U2CC exhibits greater spatial discrimination, particularly in regions with sharp thermal and hydrological gradients such as Central Anatolia, Eastern Anatolia, and Southeastern Türkiye. Because U2CC employs normalized, unitless parameters, it captures the relative displacement between temperature and precipitation extremes more effectively than DMCC or ECC.

For example, in Konya Province, DMCC yields a broadly defined “semi-arid” classification (index ≈ 12.8), whereas U2CC identifies an arid–semi-arid transition zone, aligning more closely with observed water balance characteristics and long-term drought tendencies. Similar refinements appear in transitional climates such as Bursa, Eskişehir, and Amasya, where U2CC differentiates subtle regional contrasts that DMCC and ECC classify identically.

Overall, the comparative evaluation confirms that the U2 Index minimizes classification bias introduced by temperature units, seasonal asymmetry, and rainfall variability, thereby providing a more internally consistent and spatially sensitive representation of Türkiye’s climatic patterns.

The spatial distribution maps produced by the three climate classification methods (Figs. 4–6) reveal significant methodological differences in how Türkiye’s climatic zones are delineated. Although all methods identify the broad humid–arid contrast between coastal and inland regions, their spatial sensitivities and transitional boundaries vary markedly.

DMCC Spatial Patterns

The DMCC map (Fig. 4) emphasizes large-scale continental aridity, particularly across Central Anatolia, Eastern Anatolia, and Southeastern Türkiye. However, the method tends to overestimate semi-arid conditions in the Aegean hinterland and interior basins due to its reliance on mean annual temperature and total precipitation. As a result, several regions with moderate winter rainfall—such as inner Aydın, Uşak, and parts of Afyon—are classified as more arid than their hydrological regimes suggest.

ECC Spatial Patterns

The ECC map (Fig. 5), which incorporates the mean of monthly maximum temperatures (TOM), yields slightly more humid classifications along the Aegean, Marmara, and Black Sea coastal belts. This occurs because TOM increases the relative contribution of warm-season evaporative demand, indirectly highlighting precipitation-rich coastal regions.

However, ECC underrepresents localized dry zones, particularly in topographically complex areas such as inner Antalya, Malatya, and the depressions of Eastern Anatolia, where sharp precipitation gradients occur over short distances.

U2CC Spatial Patterns

The U2 Climate Classification map (Fig. 6) provides a more balanced and spatially refined representation of Türkiye’s climatic diversity. Because U2CC is based on normalized, unitless temperature and precipitation values, it captures both macro-climatic gradients (coastal humidity vs. continental dryness) and meso-climatic variations driven by topography, elevation, and seasonal moisture fluctuations.

Notably, the U2CC map highlights several transitional belts that classical indices generalize, including:

The Afyon–Konya–Aksaray corridor, where semi-arid and slightly dry conditions alternate;

The Bursa–Bilecik–Eskişehir transitional zone, where U2CC resolves subtle humidity differences;

The Eastern Anatolia highlands, where cold continental and high-elevation patterns are distinguished.

Identification of Six Fundamental Climatic Patterns

The U2CC reveals six characteristic climatic patterns consistent with Türkiye’s established climate divisions (Section 2.3; Fig. 2):

Pattern A—Hot and arid regions: Adana, Şanlıurfa

Pattern T—Warm semi-arid regions: Konya, Afyon

Pattern X—Cold continental regions: Kars, Erzurum

Pattern KB—Humid maritime regions: Rize, Zonguldak

Pattern D—Highland regions with large annual amplitudes: Bitlis, Hakkari

Pattern EX—Extremely humid coastal zones: Giresun, Rize

These representative provinces and their corresponding long-term mean temperature, precipitation, and U2 Index values are provided in Table 3.

Verification of Spatial Patterns

The six patterns identified by U2CC were evaluated against long-term precipitation–temperature time series and were found to be consistent with classical Köppen–Geiger climate types as operationalized in modern, high-resolution datasets [6,7], as well as with observed hydrological behavior (e.g., drought intensity and seasonal moisture deficits) and orographic precipitation regimes characteristic of the Black Sea and Eastern Anatolia [17,18].

Furthermore, a Mann–Kendall trend test (p < 0.05) was applied to assess the statistical significance of long-term climatic tendencies. The results confirm that the spatial variations detected by U2CC reflect persistent regional signals rather than random fluctuations. Color Scales and Classification Intervals

The color codes and class definitions used in the DMCC, ECC, and U2CC maps (Figs. 4–6) are summarized in Table 4, enabling direct cross-method comparison.

To evaluate the internal robustness of the U2 Climate Classification, Pearson correlation analyses were conducted between province-level U2 Index values and the outputs of the two classical indices (DMCC and ECC). The results show strong and statistically significant positive correlations between U2 and both DMCC and ECC across Türkiye, confirming that the normalized structure of the U2 Index preserves the fundamental climatic gradients captured by traditional methods.

At the same time, scatter and residual analyses indicate that U2CC maintains distinct classification boundaries, particularly within transitional climatic zones where DMCC and ECC tend to converge despite substantial differences in hydrological or thermal regimes. This demonstrates that U2CC is not merely reproducing the classical schemes but providing a refined differentiation that reflects real spatial variability.

Cross-validation of the mapped classifications further confirms that U2CC aligns with observed patterns of climatic organization:

Arid and semi-arid regimes remain concentrated in Central and Southeastern Anatolia,

Humid regimes dominate the Black Sea and western coastal regions,

Highland climates of Eastern Anatolia remain distinct,

while U2CC offers sharper transitions and more consistent sub-regional boundaries compared with DMCC and ECC.

These findings collectively demonstrate that the U2 Index is statistically coherent with established climatological frameworks while improving spatial sensitivity and classification reliability.

1.

The U2 Climate Classification provides a scale-independent, unitless framework, eliminating temperature-unit bias and reducing the tendency of DMCC and ECC to overclassify humid areas.

The U2 Climate Classification provides a versatile, transferable, and physically meaningful tool for climatic assessment. Its unitless and normalization-based formulation enables integration into:

Regional planning and zoning,

Agricultural suitability and drought-risk assessments,

Water-resource management frameworks,

Climate-risk modeling and environmental monitoring,

Comparative studies across countries or datasets, without the need to adjust class thresholds.

Because U2CC provides enhanced sensitivity within transitional climatic boundaries, it offers improved diagnostic capability for identifying regions vulnerable to desertification, hydrological imbalance, and climate-induced stress. This makes U2CC a valuable complement—not a replacement—to existing classification systems, especially in regions undergoing rapid climatic change.