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The aim of this study was to construct a high-resolution (1 km × 1 km) database of precipitation, number of wet days, and number of times precipitation exceeded 10 mm and 20 mm over Greece on a monthly and on an annual basis. In order to achieve this, the ERA5 reanalysis dataset was downscaled using regression kriging with histogram-based gradient boosting regression trees. The independent variables used are spatial parameters derived from a high-resolution digital elevation model and a selection of ERA5 reanalysis data, while as the dependent variable in the training stages, we used 97 precipitation gauges from the Hellenic National Meteorological Service for the period 1980–2010. These stations were also used for validation purposes using a leave-one-out cross-validation methodology. The results of the study showed that the algorithm is able to achieve better R2 and RMSE over the standalone ERA5 dataset over the Greek region. Additionally, the largest improvements were noticed in the wet days and in the precipitation over 10 and 20 mm, where the ERA5 reanalysis dataset overestimates the number of wet days and underestimates precipitation over 10 and 20 mm, while geographically, the ERA5 dataset performs the worst in the island regions of Greece. This indicates that the ERA5 dataset does not simulate the precipitation intensity accurately over the Greek region, and using our methodology, we were able to increase the accuracy and the resolution. Our approach delivers higher-resolution data, which are able to more accurately depict precipitation in the Greek region and are needed for comprehensive climate change hazard identification and analysis.

This study investigates a range of precipitation interpolation techniques with the objective of generating high-resolution gridded daily precipitation datasets for the Greek region. The study utilizes a comprehensive station dataset, incorporating geographical variables derived from satellite-based elevation data and integrating precipitation data from the ERA5 reanalysis. A total of three different modeling approaches are developed. Firstly, we utilize a General Additive Model in conjunction with an Indicator Kriging model using only station data and limited geographical variables. In the second iteration of the model, we blend ERA5 reanalysis data in the interpolation methodology and incorporate more geographical variables. Finally, we developed a novel modeling framework that integrates ERA5 data, a variety of geographical data, and a multi-model interpolation process which utilizes different models to predict precipitation at distinct thresholds. Our results show that using the ERA5 data can increase the accuracy of the interpolated precipitation when the station dataset used is sparse. Additionally, the implementation of multi-model interpolation techniques which use distinct models for different precipitation thresholds can improve the accuracy of precipitation and extreme precipitation modeling, addressing important limitations of previous modeling approaches.