As a direct consequence of extreme monsoon rainfall throughout the summer 2022 season Pakistan experienced the worst flooding in its history. We employ a probabilistic event attribution methodology as well as a detailed assessment of the dynamics to understand the role of climate change in this event. Many of the available state-of-the-art climate models struggle to simulate these rainfall characteristics. Those that pass our evaluation test generally show a much smaller change in likelihood and intensity of extreme rainfall than the trend we found in the observations. This discrepancy suggests that long-term variability, or processes that our evaluation may not capture, can play an important role, rendering it infeasible to quantify the overall role of human-induced climate change. However, the majority of models and observations we have analysed show that intense rainfall has become heavier as Pakistan has warmed. Some of these models suggest climate change could have increased the rainfall intensity up to 50%. The devastating impacts were also driven by the proximity of human settlements, infrastructure (homes, buildings, bridges), and agricultural land to flood plains, inadequate infrastructure, limited ex-ante risk reduction capacity, an outdated river management system, underlying vulnerabilities driven by high poverty rates and socioeconomic factors (e.g. gender, age, income, and education), and ongoing political and economic instability. Both current conditions and the potential further increase in extreme peaks in rainfall over Pakistan in light of anthropogenic climate change, highlight the urgent need to reduce vulnerability to extreme weather in Pakistan.

Abstract. In the 2022 summer, West-Central Europe and several other northern-hemisphere mid-latitude regions experienced substantial soil moisture deficits in the wake of precipitation shortages and elevated temperatures. Much of Europe has not witnessed a more severe soil drought since at least the mid-20th century, raising the question whether this is a manifestation of our warming climate. Here, we employ a well-established statistical approach to attribute the low 2022 summer soil moisture to human-induced climate change, using observation-driven soil moisture estimates and climate models. We find that in West-Central Europe, a June–August root-zone soil moisture drought such as in 2022 is expected to occur once in 20 years in the present climate, but would have occurred only about once per century during pre-industrial times. The entire northern extratropics show an even stronger global warming imprint with a 20-fold soil drought probability increase or higher, but we note that the underlying uncertainty is large. Reasons are manifold, but include the lack of direct soil moisture observations at the required spatiotemporal scales, the limitations of remotely sensed estimates, and the resulting need to simulate soil moisture with land surface models driven by meteorological data. Nevertheless, observation-based products indicate long-term declining summer soil moisture for both regions, and this tendency is likely fueled by regional warming, while no clear trends emerge for precipitation. Finally, our climate model analysis suggests that in a 2 °C world, 2022-like soil drought conditions would become twice as likely for West-Central Europe compared to today, and would take place nearly every year across the northern extratropics.

Ensemble weather forecasts often under‐represent uncertainty, leading to overconfidence in their predictions. Multi‐model forecasts combining several individual ensembles have been shown to display greater skill than single‐ensemble forecasts in predicting temperatures, but tend to retain some bias in their joint predictions. Established postprocessing techniques are able to correct bias and calibration issues in univariate forecasts, but are generally not designed to handle multivariate forecasts (of several variables or at several locations, say). We propose a flexible multivariate Bayesian postprocessing framework, based on a directed acyclic graph representing the relationships between the ensembles and the observed weather. The posterior forecast is inferred from available ensemble forecasts and an estimate of the shared discrepancy, obtained from a collection of past forecast–observation pairs. We also propose a novel approach to selecting an appropriate training set for estimation of the required correction, using synoptic‐scale analogues to obtain a regime‐dependent estimate of the adjustment. The proposed technique is applied to forecasts of surface temperature over the UK during the winter period from 2007 to 2013. Although the resulting parametric multivariate‐normal probabilistic forecasts are marginally less sharp than those of the leading competitor, they capture the spatial structure of the observations better than a correlation structure based on either the ensembles or climatology alone, and are robust to changes in the variables and spatial domain of the forecast, at a greatly reduced computational cost.

The 2021 Met Office Climate Data Challenge hackathon series provided a valuable opportunity to learn best practice from the experience of running online hackathons uniquely characterised by the challenges faced by climate data science in the wake of the COVID‐19 pandemic. In particular, the University of Bristol CMIP6 Data Hackathon with over 100 participants from the United Kingdom highlights the advantages of participating in such events as well as lessons learned. A suggested methodology to structure, plan, promote and ensure longevity of the hackathon outputs is described ensuring smoother running of future events.

Risk modellers in the insurance industry use catastrophe models to estimate the distribution of possible damage from natural catastrophes. The output from catastrophe models is often adjusted to create alternative risk scenarios. These adjustments are made for many reasons, such as to reflect different scientific hypotheses, different interpretations of historical data or different scenarios related to climate variability and climate change. Models that present the output in a list of simulated synthetic events with their associated damage (so‐called event loss tables) can be adjusted rather easily, since information about desired adjustments is typically expressed in terms of changes in the properties of events. Models that present the output in a list of simulated synthetic years (so‐called year loss tables) are harder to adjust, however, because the occurrences of the events are hard‐wired into the simulated years. A method is described that allows the adjustment of the results in a year loss table by the application of weights to the years. The weights are calculated in such a way as to capture the specified changes in properties of the underlying events. The method is demonstrated by applying it to output from a catastrophe model and using it to quantify the changes in US hurricane wind damage due to shifts between long‐term average, active and inactive levels of hurricane activity. It is shown that the method works well by comparing the results with more accurate results derived directly from the underlying event loss table.