As a form of ``small AI'', quantile statistical learning is used to forecast diurnal and nocturnal Q(.90) air temperatures for Paris, France from late spring to late summer months of 2020. The data are provided by the Paris-Montsouris weather station. Rather than trying to directly anticipate the onset and cessation of reported heat waves, Q(.90) values are estimated because the 90th percentile requires that the higher temperatures be relatively rare and extreme. Predictors include eight routinely available indicators of weather conditions, lagged by 14 days; the temperature forecasts are produced two weeks in advance. Conformal prediction regions capture forecasting uncertainty with provably valid properties. For both diurnal and nocturnal temperatures, forecasting accuracy is promising, and sound measures of uncertainty are provided. Benefits for policy and practice follow.

翻译：作为一种"小型人工智能"方法，分位数统计学习被用于预测2020年春末至夏末期间法国巴黎的昼夜Q(.90)气温。数据由巴黎蒙苏里气象站提供。本研究未直接预测已报告热浪的起始与终止时间，而是选择估计Q(.90)值，因为第90百分位数要求较高温度具有相对罕见性和极端性。预测因子包括14个滞后14天的常规天气指标；温度预测可提前两周生成。保形预测区域通过可证明的有效特性捕捉预测不确定性。对于昼夜温度的预测精度均表现良好，并提供了可靠的不确定性度量方法。该方法对政策制定与实践应用具有积极意义。