To assess the role of climate change in individual weather events, different lines of evidence need to be combined in order to draw robust conclusions about whether observed changes can be attributed to anthropogenic climate change. Here we present a transparent method, developed over 8 years, to combine such lines of evidence in a single framework and draw conclusions about the overarching role of human-induced climate change in individual weather events.

We investigate how to make statistical predictions of extreme weather such that events predicted to occur with a probability of 1 % will occur 1 % of the time. We apply the methods we describe to a standard extreme weather attribution example from the recent climate literature. We find that the methods we describe imply that extremes are roughly twice as likely as when estimated using maximum likelihood. We have developed a software package to make it easy to apply these methods.

In this study, we investigated the performance of Gaussian process regression (GP) models in handling outlier-affected spatial datasets. Our findings emphasized that models with the proposed methods provided accurate predictions and reliable uncertainty quantification, showcasing resilience against outliers. Overall, our study contributes to advancing the understanding of GP regression in spatial contexts and offers practical solutions to enhance its applicability in outlier-rich environments.

Correctly forecasting weather is crucial for decision-making in various fields. Standard multivariate verification tools have limitations, and a single tool cannot fully characterize predictive performance. We formalize a framework based on aggregation and transformation to build interpretable verification tools. These tools target specific features of forecasts, improving predictive performance characterization and bridging the gap between theoretical and physics-based tools.

We present a novel stochastic weather generator conditioned on circulation patterns and regional temperature, accounting for dynamic and thermodynamic atmospheric changes. We extensively evaluate the model for the central European region.  It statistically downscales precipitation for future periods, generating long, spatially and temporally consistent series. Results suggest an increase in extreme precipitation over the region, offering key benefits for hydrological impact studies.

Global warming is a big issue: it is necessary to know more details to make a forecast model and plan adaptation measures. Warming varies in space and time and models often average it over large areas. However, it shows great variations between months of the year. It also varies between regions of the world and between lowland and highland regions. This paper uses statistical and machine learning techniques to quantify such differences between Italy and the UK at different altitudes.

This paper assesses the skill of probabilistic forecasts of solar irradiance in the northern regions of Chile. Raw ensemble forecast are calibrated using a parametric and a novel non-parametric machine-learning-based method. As the reference approach, the ensemble model output statistics are considered. We verify the superiority of the proposed non-parametric neural-network-based ensemble correction, resulting in more than 50 % improvement in prediction performance compared to the raw forecasts.

Drought frequently affects southern Ethiopia, and inadequate rainfall data complicate monitoring. Satellite data from three sources were evaluated for accuracy, showing the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) to be the most reliable. We assessed drought severity using the standardized precipitation index, aligning results with historical drought patterns. The study emphasizes that the CHIRPS enhances drought monitoring and early-warning systems in the region.

We employ hierarchical statistical models to investigate spatiotemporal differences between sea surface temperature, marine air temperature, and their anomalies in the tropical Pacific.

This paper presents a novel study on boxplot-valued data in climatological applications. Our methodologies are applied to the Berkeley Earth Surface Temperature Study. We validate our approaches through comprehensive simulations, comparing Bayesian and frequentist estimators for efficiency and accuracy. The results provide robust insights into climatic trends, particularly summer average temperatures across European countries. 

Southeasterm Australia, including the Murray–Darling Basin, is a highly productive agricultural region largely dependent on adequate rainfall, providing irrigation water needed for crops.

The Australian Bureau of Meteorology uses linear methods and provides seasonal forecasts expressed as the probability of exceeding median rainfall. I use expanded methods, including more ocean data and deep learning neural networks that provide nonlinear estimates of the rainfall as measured by rain gauges.

Meteorological data recorded at high frequency by automatic sensors are often marred by multiple forms of error. Existing validation techniques, in isolation, are sub-optimal for such error-prone data. We propose a new data-standardization procedure for the validation of strongly correlated series which commonly arise in meteorology. We show the procedure to be more comprehensive in the simultaneous detection of solitary spikes, shifts in series means, and irregular diurnal patterns.