We develop a spatial statistical calibration of wind gust observations for the region of Germany with an interpolation to unobserved locations. Furthermore, the model is spatially adaptive and includes the station altitude both as explanatory variable and as offset to increase the distance between stations. This offset allows us to include mountain stations into the training data. Compared to a spatially constant model, the adaptive model improves the representation of extreme wind gusts.

Extreme precipitation probability estimation is vital for hazard protection design but has high uncertainty. We tested six statistical models using 2000 years of climate data. Our Bayesian hierarchical duration-dependent Generalized Extreme Value model shows the highest accuracy and robustness for sample sizes between 30 and 100 years, making it highly promising for use with limited observational records.

Using information derived from tree-rings, we reconstruct possible combinations of past temperatures and precipitation amounts.  This lets us put current changes in context and shows, for example, that the 1930s were likely the driest decade on record in central Kansas, while the late 20th century was likely the wettest period on record in the North American southwest.

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. 

This study investigates soil moisture–temperature coupling during the extreme warm conditions in May–August 2018 in southern and central Sweden using the merged GLEAM-E-OBS dataset and four simulations from the Weather Research and Forecasting model coupled with the Community Terrestrial Systems Model (WRF-CTSM). Based on changes in surface soil moisture, evaporative fraction, and daily maximum 2 m temperature, on average across the region and five datasets, the coupling lasted for 22 d.

We studied how small, short-lived ocean features in the Mediterranean Sea affect microscopic plant communities that support ocean life. Using a new statistical approach, we found strong evidence that these features can host unique communities not found in surrounding waters. This discovery helps us better understand the role of ocean dynamics in shaping marine ecosystems, even when data are limited and conditions vary widely.

Standard algorithms for estimating the power spectral density of finite discrete data require interpolating missing samples, which usually produces biased estimates. An unbiased estimate can be obtained by taking the Fourier transform of the unbiased estimator of the circular autocorrelation, using only the available data. With missing samples, this estimator can produce negative power spectral densities, but converges to positive values when averaged over a sufficient number of realizations.

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.

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.

This study compares different methods to predict ocean surface currents in a fast-changing strait. Using over three years of radar observations, we tested both traditional and modern data-driven approaches. The results show that advanced learning models give the most accurate forecasts, especially for several hours ahead. We also find that including enough past information improves predictions. These findings can help improve maritime safety and support better decision-making in coastal areas.

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 examines how ocean wave conditions around France may change as the climate warms. Using a new statistical approach and multiple climate projections, it shows that extreme waves are likely to become more intense in winter and less severe in summer, especially along the Atlantic coast and the English Channel. The work also proposes a new way to define extreme sea-state conditions over the structure's lifetime, leading to better design.

The aim of this study is to develop efficient estimation methods of the maximum likelihood estimation method for the q-Pareto distribution (q-PD) parameters that are based on numerical methods to maximize the log-likelihood of the q-PD. And the q-PD parameters is a q-exponential distribution, finding applications in many fields such as:  seismology (earthquake patterns).

The attribution of extreme events typically focuses on individual events, such as heatwaves. In our study, we extend this approach to include complete time series during such events in order to capture their temporal dependence. Using today's knowledge, we find that the influence of anthropogenic greenhouse gas emissions on heatwaves could already have been proven in the 1960s. It is very likely that 21st-century heatwaves over Europe would be impossible without these emissions.