In this paper, the annual rainfall and temperature values, measured in the period 1951-2016 in a region of southern Italy (Calabria), have been spatially interpolated using deterministic and geostatistical techniques in an R environment. In particular, Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Kriging with External Drift (KED) and Ordinary Cokriging (COK) were compared to evaluate the best suitability method in reproducing the actual surface. Then, the spatial variation of aridity in Calabria has been evaluated using the De Martonne aridity index (IDM), which is based on rainfall and temperature data. As a result, geostatistical methods incontrovertibly show a better estimate than the IDW. Specifically, the KED was identified as the best predictor method for both rainfall and temperature data. Moreover, the spatial distribution of the IDM evidenced that the majority of the study area can be classified as humid, with semi–arid conditions mainly identified in the coastal areas.

本文在R环境中使用确定性和地统计学方法对意大利南部（卡拉布里亚）一个地区在1951-2016年期间测得的年降雨量和温度值进行了空间插值。特别是，比较了反向距离权重（IDW），普通克里格法（OK），带外部漂移的克里格法（KED）和普通共克里格法（COK），以评估在再现实际表面时的最佳适用性方法。然后，已使用基于降水和温度数据的De Martonne干旱指数（IDM）对卡拉布里亚的干旱空间变化进行了评估。结果，与IDW相比，地统计学方法无疑显示出更好的估计。特别是，KED被确定为降雨和温度数据的最佳预测方法。此外，