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 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.

Weather generators efficiently create realistic weather data based on historical records. This study introduces a daily temperature generator for large regions, separating deterministic factors (trends, seasonality) from random variations modeled using space-time interactions. Validated on French weather station data, it replicates observed patterns, including heatwaves. It offers a practical solution for generating realistic weather data, for applications such as climate impact assessments.

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.