## 2 Materials and Methods  
In this study, an innovative hourly observational database is used to calculate the number of hours within different temperature intervals, i.e., to assess the empirical distribution of hourly temperatures. Furthermore, the number of hours per different temperature intervals of eight fruit species are analyzed separately, giving more emphasis to grapevines and olive trees, owing to their key role in the Portuguese economy. The hourly temperature dataset used was obtained from the Portuguese national weather service's (Instituto Portugues do Mar e da Atmosfera, IPMA) network of meteorological stations in mainland Portugal. Of the original 133 stations, those with more than 20% of missing hourly data were rejected beforehand. Thus, 63 stations were selected for the 19-year period 2000-2018 (Figure 1). The geographical locations of the selected stations are shown in Figure 1. In general, these stations are representative of the altitudinal variation within the territory (Table S1 and Figure 1). The station with the highest elevation, Penhas Douradas (1380 m), is located in Serra da Estrela (the highest mountain range in mainland Portugal), while the station with lowest elevation is located in Figueira da Foz (4 m). The southernmost weather station is Sagres (37 deg01' N), while the northernmost is Moncao (42 deg06' N). Regarding the longitude, it varies from 9 deg48' W in Cabo Raso to 6 deg27' W in Miranda do Douro (Table S1). A preliminary quality-checking of data was carried out. Box-plots of each station time series were plotted to represent the full empirical distributions of observed data and to detect possible erroneous values (not shown). Among the 63 selected stations, 21 stations have less than 5% of missing hourly data, 22 stations between 5% and 10%, 15 stations between 10% and 15%, and 5 stations between 15% and 20% (Figure S1). Hourly data were obtained by averaging 10-min instant data at each weather station. A two-step methodology was followed in order to obtain the final maps of the number of hours within each temperature interval: (1) a geostatistical temperature interpolation in order to capture the spatial variability of the number of hours for different temperature intervals, and (2) a bias correction of the interpolated data. First, the number of hours for each different temperature interval was calculated from the long-term means (baseline of 2000-2018) of the hourly temperatures at each weather station separately. The following temperature intervals were selected: \(\geq\)4-\(<\)8 deg, \(\leq\)8-\(<\)12 deg, \(\geq\)12-\(<\)16 deg, \(\geq\)16-\(<\)20 deg, \(\geq\)20-\(<\)24 deg, \(\geq\)24-\(<\)28 deg, \(\geq\)28-\(<\)32 deg, and \(\geq\)32-\(<\)36 deg. The number of hours with temperature \(<\)4 deg and \(>\)36 deg was excluded from this study, because they occur in only a few days of the year and with a restricted geographic distribution, thus not warranting a statistically robust analysis. In order to obtain the number of hours in different temperature intervals, the long-term means for the baseline 2000-2018 were chosen, as they allow overcoming possible biases in the database, e.g., owing to missing data. Furthermore, the aggregation of all years was the best option for this study purposes, which is focused on spatial variability rather than on temporal variability. However, through a sensitivity analysis shown in Figure S2, it was possible to attest that the choice of the hourly climatological means instead of the whole raw hourly data had only a negligible impact on the relative distribution of the number of hours (shape of the empirical distributions), showing differences of less than 2% in the relative number of hours for all temperature intervals and for all meteorological stations. Second, the number of hours in each temperature interval and for all selected sites were subject to ordinary least squares (OLS) linear regressions, with three independent variables: latitude, elevation, and Euclidean distance to the coastline (at 1 km resolution).