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Climate model output has systematic errors which can be reduced with statistical methods. We review existing bias-adjustment methods for climate data and discuss their skills and issues. We define three demands for the method and then evaluate them using real and artificially created daily temperature and precipitation data for Austria to show how biases can also be introduced with bias-adjustment methods themselves.

The intensities of recent heatwave events, such as the record-breaking heatwave in early June 2021 in the Pacific Northwest area, are substantially altered by climate change. We further quantify the contribution of the local weather situation and the land surface conditions with a statistical model suited for extreme data. Based on this method, we can answer what if questions, such as estimating the change in the 2021 heatwave temperature if it happened in a world without climate change.

In this paper we consider the problem of detecting changes in the distribution of the annual maximum temperature, during the years 1950–2018, across Europe. We find that, on average, the temperature that would be expected to be exceeded approximately once every 100 years in the 1950 climate is expected to be exceeded once every 6 years in the 2018 climate. This is of concern due to the devastating effects on humans and natural systems that are caused by extreme temperatures.

Ocean wave climate has a significant impact on human activities, and its understanding is of socioeconomic and environmental importance. In this study, we propose a statistical model that predicts wave heights in a location in the Bay of Biscay. The proposed method allows us to understand the spatiotemporal relationship between wind and waves and predicts well both wind seas and swells.

This paper uses the tail pairwise dependence matrix (TPDM) proposed by Cooley and Thibaud (2019), which we extend to the description of common extremes in two variables. We develop an extreme pattern index (EPI), a pattern-based aggregation to describe spatially extended weather extremes. Our results show that the EPI is suitable for describing heat waves.  We extend the EPI to describe extremes in two variables and obtain an index to describe compound precipitation deficits and heat waves.

This paper introduces a method to assess whether two data sets come from the same source. Current methods do not adequately consider spatial and temporal correlations and their annual cycles in a comprehensive test. This method addresses that gap, thereby providing a new and rigorous tool for evaluating the realism of climate simulations and measuring changes in variability over time.

To ensure photovoltaic (PV) plants last, we need to understand how climate change affects solar radiation. Climate models help predict future radiation for PV plants, but there is often uncertainty. We explore this uncertainty and its impact on building PV plants. We highlight the importance of considering uncertainties for accurate planning and management. A California case study shows a practical application for the solar industry.

This study uses tree-based machine learning (ML) to forecast PM_2.5 in a complex terrain region. The models show the potential to predict pollution events with several hours of anticipation, and they integrate multiple sources of information, including in situ stations, satellite data, and deterministic model outputs. The importance analysis helps understand the processes affecting air quality in the region and highlights the relevance of external sources of pollution in PM_2.5 predictability.

In hydrology, the probability distributions are used to determine the probability of occurrence of rainfall events. In this study, two different methods for modeling rainfall time characteristics have been applied: a direct method and an indirect method that make it possible to relax the assumptions of the renewal process. The analysis was extended to two additional time variables that may be of great interest for practical hydrological applications: wet chains and dry chains.