Forecasting Extreme High Summer Temperatures in Paris and Cairo Using Gradient Boosting and Conformal Prediction Regions

TL;DR

This study employs gradient boosting and conformal prediction to forecast extreme high summer temperatures in Paris and Cairo, providing two-week ahead predictions and addressing uncertainty for rare heat events.

Contribution

It introduces a novel application of gradient boosting combined with conformal prediction regions for forecasting extreme summer temperatures in two different climate zones.

Findings

Promising accuracy in predicting extreme heat in Paris.

Some progress in forecasting record-breaking hot days in Cairo.

Effective use of lagged weather indicators for two-week forecasts.

Abstract

In this paper, gradient boosting is used to forecast the Q(.95) values of air temperature and the Steadman Heat Index. Paris, France during late the spring and summer months is the major focus. Predictors and responses are drawn from the Paris-Montsouris weather station for the years 2018 through 2024. Q(.95) values are used because of interest in summer heat that is statistically rare and extreme. The data are curated as a multiple time series for each year. Predictors include seven routinely collected indicators of weather conditions. They each are lagged by 14 days such that temperature and heat index forecasts are provided two weeks in advance. Forecasting uncertainty is addressed with conformal prediction regions. Forecasting accuracy is promising. Cairo, Egypt is a second location using data from the weather station at the Cairo Internal Airport over the same years and months. Cairo is a more challenging setting for temperature forecasting because its desert climate can create abrupt and erratic temperature changes. Yet, there is some progress forecasting record-setting hot days.