Forecasting Extreme Day and Night Heat in Paris

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.