## 2 Materials and Methods
The aim was to perform a regression analysis (one for each temperature interval) with the number of hours as dependent variable and the geographical variables (latitude, elevation, and distance to coastline) as exploratory variables. From the multiple regression analysis, the regression coefficients were obtained and map algebra tools in GIS were applied to produce the final maps from the gridded variables at a 1 km grid resolution. The resulting OLS maps presented the inherent error of the regression adjustment, i.e., they explain the influence of the geographic variables on the model [22]. On the other hand, the residuals of the regression fit at each meteorological station tend to reflect other factors beyond the independent variables, such as soil cover properties, local winds (e.g., breezes), or other mesoscale systems [1]. These factors may also influence the model, underlying important biases between interpolated and observed values. Hence, to improve the OLS results, bias corrections were carried out based on the number of hours for the different temperature intervals, computed for the network of 63 meteorological stations and over the same baseline period (2000-2018). An inverse distance weighted (IDW) interpolation of the biases (residuals) to the 1 km grid was then added to the OLS of the number of hours, separately for each temperature interval. The IDW interpolation determines each grid point value using a linearly weighted combination of a set of sample points. The weight is a function of the inverse distance, assuming that the variable value being mapped decreases with the distance [23; 24]. Kriging, spline, and polynomial interpolations were also tested, but the IDW method was eventually chosen, since it provided the lowest mean squared error (not shown). The aforementioned procedure was independently repeated for the number of hours within the different temperature intervals. The percentages of hours by the different temperature intervals in a year (relative frequencies of occurrence) were then calculated and their spatial variability was examined. The thermal conditions associated with different key agricultural and forest species in Portugal were assessed. A total of eight species were selected: almond tree (_Prunus dulcis D. [PERSON]_), carob tree (_Ceratonia siliqua_ L.), chestnut tree (_Castanea sativa Mill._), citrus fruit species (e.g., oranges, lemons, Clementine's and mandarins), cork oak (_Quercus suber_ L.), holm oak (_Quercus ilex_ L.), olive tree (_Olea europea_ L.), and grape vines (_Vitis vinifera_ L.). Their land coverage was retrieved from the fifth level of Land Use and Occupation Map: COS2007 (Source: Direcao-Geral do Territorio, [[http://www.dgtrritorio.pt/](http://www.dgtrritorio.pt/)]([http://www.dgtrritorio.pt/](http://www.dgtrritorio.pt/))). Owing to the high relevance of olive trees and vines to the Portuguese economy, they were specifically analysed within their main demarcated regions. This procedure enabled to highlight the prevailing thermal conditions in each region. For this purpose, the 50 Denominations of Origin (DOs) or sub-regions for the grapevines, and the 6 Protected Designations of Origin for olive trees (PDOs) were considered. As for the other species there are no protected designations of origin, it was not possible to apply the same methodology.