Recurrent flooding in the Marahoué region, especially at Bouaflé and Zuénoula, requires reliable rainfall forecasts. This study uses deep learning with satellite data to predict daily rainfall up to seven days ahead. The results show high accuracy for short- and medium-term forecasts, supporting early warning systems and helping local communities prepare for flood risks.

Precipitation generators are statistical models for generating long synthetic sequences of (multisite) precipitation for hydrological analyses. One widely-used class of precipitation generators is based on so-called 'generalised linear models'.  In this work, we extend this class to better capture key features of daily precipitation and introduce a new method to ensure realistic inter-site dependence, so neighbouring locations tend to be dry or wet at the same time.

The analysis of the effect of extreme meteoceanic conditions is usually based on physical simulators, which rely on simulated extreme inputs consistent with the observations. However, surge measurements often fail to meet the theoretical assumptions. To address this, we propose a new simulation method which makes it possible to adjust the desired level of extremes after retrieving standard hypotheses. The consistency of simulations with the observations is then validated by using several tools.

This study advances nonstationary precipitation modeling by using single, flexible distributions to analyze trends across the full daily spectrum. We demonstrate that evolving shape parameters are critical for accurately capturing observed differential changes in low, medium, and extreme quantiles. This method ensures statistical consistency, providing reliable trend assessments over the two-component framework common in climate impact analysis. 

We derived a new statistical test that can compare climate models and observations more broadly than before, while allowing for both natural fluctuations and human influences. Tests on global temperature data show that most climate models differ from observations in important ways.