Local scale estimates of temperature change in the twenty-first century are necessary for informed decision making in both the public and private sector. In order to generate such estimates for Chile, weather station data of the Dirección Meteorológica de Chile are used to identify large-scale predictors for local-scale temperature changes and construct individual empirical-statistical models for each station. The geographical coverage of weather stations ranges from Arica in the North to Punta Arenas in the South. Each model is trained in a cross-validated stepwise linear multiple regression procedure based on (24) weather station records and predictor time series derived from ERA-Interim reanalysis data. The time period 1979–2000 is used for training, while independent data from 2001 to 2015 serves as a basis for assessing model performance. The resulting transfer functions for each station are then directly coupled to MPI-ESM simulations for future climate change under emission scenarios RCP2.6, RCP4.5 and RCP 8.5 to estimate the local temperature response until 2100 A.D. Our investigation into predictors for local scale temperature changes support established knowledge of the main drivers of Chilean climate, i.e. a strong influence of the El Niño Southern Oscillation in northern Chile and frontal system-governed climate in central and southern Chile. Temperature downscaling yields high prediction skill scores (ca. 0.8), with highest scores for the mid-latitudes. When forced with MPI-ESM simulations, the statistical models predict local temperature deviations from the 1979–2015 mean that range between − 0.5–2 K, 0.5–3 K and 2–7 K for RCP2.6, RCP4.5 and RCP8.5 respectively.

要在公共部门和私营部门做出明智的决策，必须对二十一世纪的当地温度变化进行规模估算。为了生成智利的此类估计，使用智利Dirección气象卫星站的气象站数据来识别当地尺度温度变化的大规模预测因子，并为每个气象站构建各自的经验统计模型。气象站的地理覆盖范围从北部的阿里卡到南部的蓬塔阿雷纳斯。每个模型都基于（24）个气象站记录和从ERA-Interim重新分析数据得出的预测器时间序列，在交叉验证的逐步线性多元回归程序中进行训练。1979-2000年的时间用于培训，而2001年至2015年的独立数据则是评估模型绩效的基础。然后，将每个站所产生的传递函数直接耦合到MPI-ESM模拟中，以预测排放情景RCP2.6，RCP4.5和RCP 8.5下的未来气候变化，以估计直到2100 AD的局部温度响应。变化支持对智利气候主要驱动因素的既定知识，即智利北部的厄尔尼诺南方涛动的强烈影响以及智利中南部的额叶系统控制的气候。温度降低会产生较高的预测技能得分（约0.8），中纬度得分最高。当采用MPI-ESM模拟时，统计模型预测1979-2015年的局部温度偏差意味着RCP2.6，RCP4.5和RCP8的范围在− 0.5–2 K，0.5–3 K和2–7 K之间。 5个。