Abstract
The association between elevation and temperature is analysed by simple linear correlations across several spatial scales. The minimum (tn) and maximum (tx) temperatures (response variables), expressed at two time scales (monthly and daily), are observed for 102 weather stations in east central France from 1980 to 2014 (12,784 days). Elevation (explanatory variable) is provided at 10 resolutions: 50, 100, 200, 500 m, 1, 2, 4, 8, 12, and 16 km. The coefficient of determination, R 2 , is used to determine which resolution gives the best results. The slope given by the regression is used to assess the drop in temperature per unit of elevation (temperature lapse rate [TLR]). In most situations, monthly and daily temperatures are optimally explained by the finest (50 m) resolution: the R 2 is, respectively, 0.53 and 0.24 for tn and 0.78 and 0.39 for tx. The coarser resolutions produce results of much lower quality. However, in one circumstance (monthly mean of tn), the highest R 2 value is obtained for the 4‐km resolution, which is a meaningful result as current regional climate models now achieve similar resolutions. Both monthly and daily TLRs of tn and tx are, on average, slightly lower than −0.5 °C/100 m at 50‐m resolution. The TLR decreases with resolution: it is only −0.23 °C/100 m for tn and −0.13 °C/100 m for tx at 16‐km resolution. Other insightful results involve the influence of the topographical context, which shows some additional effect with that of elevation and which was quantified through partial correlations.