Evaluating the Representativeness of Interpolated Climate Data at Vineyard Scale in Complex Terrain: Evidence from Two Hungarian Wine Regions

Accurate characterization of vineyard-scale climate is essential for understanding grapevine development, yield formation, and wine quality. However, climate analyses in viticulture are still largely based on interpolated macroclimate datasets, which may not adequately represent local conditions. This study evaluated the ability of commonly used interpolation methods to reproduce microclimatic conditions across vineyards, focusing on key meteorological variables and spatial resolutions. Daily values extracted from macroclimate grids (1/6° and 0.1°) and high-resolution (1 km) interpolated datasets were compared with in situ microclimate observations. The analysis covered temperature (minimum, mean, and maximum), vapor pressure deficit (VPD), and global radiation, using statistical metrics including correlation (r), root mean square error (RMSE), bias, and distribution-based error characteristics. The results reveal a strong variable-dependent performance of interpolation methods. Global radiation showed moderate to strong agreement between macro- and microclimate datasets, whereas temperature—particularly minimum temperature—exhibited substantial discrepancies. These differences are primarily attributed to local processes such as cold air pooling, topographic effects, and canopy-level interactions, which are not resolved by coarse-scale datasets. An exploratory Mean Topographic Association Score (MTAS) analysis further identified the Topographic Position Index (TPI) as the terrain descriptor showing the strongest overall association with interpolation errors across the investigated meteorological variables. Among the evaluated approaches, the 0.1° climate product generally provided the best overall agreement with vineyard observations for temperature-related variables, substantially reducing interpolation errors compared with the original 1/6° dataset. For example, the RMSE of Tmin decreased from 3.85 °C to 2.41 °C. Nevertheless, considerable residual errors remained, indicating persistent limitations of interpolation approaches in complex terrain. VPD deviations reflected the combined influence of temperature- and humidity-related uncertainties, highlighting the sensitivity of derived variables to local climatic conditions. The findings demonstrate that interpolated macroclimate datasets should be applied with caution in vineyard-scale analyses, especially for variables sensitive to local processes. For applications such as phenological modelling, climate suitability assessment, and precision viticulture, the integration of high-resolution data sources or direct microclimate measurements is essential. This study highlights the limitations of interpolated climate products in complex vineyard terrain and demonstrates that consideration of local terrain configuration, particularly relative topographic position, together with vineyard-scale observations and high-resolution climate information, is essential for reliable viticultural climate assessments.