## 2 Materials and Methods
### Input Data  
The model requires a large range of input data, such as daily weather data, typical soil properties, topographic features, varietal characteristics and crop management information. The required daily variables comprise daily maximum air temperature (Tmax), daily minimum air temperature (Tmin), daily accumulation of solar radiation (Rad), daily precipitation total (Prec), daily mean wind speed (Wspeed), daily mean relative humidity (Rh) and CO\({}_{2}\) level. These data were obtained from two different observational sources. Tmax, Tmin and Prec were obtained from the E-OBS dataset, version 19e [41], whilst Rad, WSpeed and Rh were retrieved from the ERA5 dataset [42]. These two datasets have been widely used and validated by many previous studies. Data were extracted at 0.1\({}^{\circ}\) latitude-longitude regular grids (spatial resolution of approximately 10 km) for the baseline period of 1986-2015.  
Soil data, like soil texture and pH, were obtained from the Harmonized World Soil Database HWSD and [43], available from two layers (depths 0-30 cm and 30-100 cm). Some soil properties for STICS were estimated by using pedotransfer functions [44], such as soil albedo, runoff, soil permeability, field capacity, wilting point and bulk density. Additionally, some soil properties were set as standard, following [PERSON], et al. [31]: initial soil water content (set at field capacity); maximum unimpeded root depth (200 cm), initial root density (0.05 cm-cm-3) and soil organic N content (6% of dry soil). Terrain data, such as slope degree and orientation, were obtained from the GTOPO30 digital elevation model and using GIS techniques ([[https://lta.cr.usgs.gov/GTOPO30](https://lta.cr.usgs.gov/GTOPO30)]([https://lta.cr.usgs.gov/GTOPO30](https://lta.cr.usgs.gov/GTOPO30))).  
For a large-scale comprehensive modelling approach throughout Europe, some assumptions were made concerning cultivated varieties and cultural practices. As it will not be possible to analyze all the specific varieties grown at each location, which would also impede a comparative analysis among regions, a standard variety was considered for all locations. The selected variety was cv. Pinot noir, due to its early-to-intermediate ripening and moderate yields, being suitable for a large number of viticultural regions [45]. Pinot noir is currently the 10 th most planted variety and the 7 th fastest-expanding variety in the world [46]. Since it is grown in vineyards throughout Europe, from the warmer countries in Southern Europe to the cooler Central-Northern European wine regions [46], it was chosen herein. Furthermore, this variety was already validated in a previous study [35].  
Cultural practices were kept invariant in the model runs, and crop interventions were set to a minimum (i.e., topping, thinning and leaf removal were not considered in the model). The default trellis system was Cordon, and vine density was set to 4000 vines ha-1, a reasonable value considering most commercial vineyards in Europe [47]. The technological harvest date was determined once the berry water content reached a maximum of 77%, corresponding to a probable alcohol level of 12.5 (% _v/v_) [40].