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Figure S1. Prediction of maximum temperature taking into account multiple processes that operate at landscape and local scales to modify thermal regimes. Locations of weather stations used to build the models are indicated on the maximum temperature surface.

Figure S2. Variation in monthly maximum temperature explained by three models. Equation 1 is derived from a base set of predictor variables. Equation 2 and 3 included the addition of the predictor variables cloud and wind exposure respectively.

Figure S3. Spatial representation of the relative contribution of seven monthly surfaces that directly informed maximum temperature of any warmest period at each pixel. Of the monthly surfaces, January 2007 and December 2008 were the largest contributors and informed 30% and 44% of pixels respectively in the amalgamated surface.

Table S1. Statistics for monthly temperature models. Independent predictor variables for temperature found to be statistically significant at <0.05 level are highlighted in blue (negative effect) and red (positive effect). Note: I=intercept; B=slope of the regression line; P=significance of regression line; Adj Rsqr=adjusted r2; n=number of weather stations used in model.

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