Extreme event attribution aims to elucidate the link between global climate change, extreme weather events, and the harms experienced on the ground by people, property, and nature. It therefore allows the disentangling of different drivers of extreme weather from human-induced climate change and hence provides valuable information to adapt to climate change and to assess loss and damage. However, providing such assessments systematically is currently out of reach. This is due to limitations in attribution science, including the capacity for studying different types of events, as well as the geographical heterogeneity of both climate and impact data availability. Here, we review current knowledge of the influences of climate change on five different extreme weather hazards (extreme temperatures, heavy rainfall, drought, wildfire, tropical cyclones), the impacts of recent extreme weather events of each type, and thus the degree to which various impacts are attributable to climate change. For instance, heat extremes have increased in likelihood and intensity worldwide due to climate change, with tens of thousands of deaths directly attributable. This is likely a significant underestimate due to the limited availability of impact information in lower- and middle-income countries. Meanwhile, tropical cyclone rainfall and storm surge height have increased for individual events and across all basins. In the North Atlantic basin, climate change amplified the rainfall of events that, combined, caused half a trillion USD in damages. At the same time, severe droughts in many parts of the world are not attributable to climate change. To advance our understanding of present-day extreme weather impacts due to climate change developments on several levels are required. These include improving the recording of extreme weather impacts around the world, improving the coverage of attribution studies across different events and regions, and using attribution studies to explore the contributions of both climate and non-climate drivers of impacts.

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.

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Capsule: Currently no systematic assessment of loss and damage due to climate change exists. Towards such an inventory we present a transparent way to ascertain the quality of evidence for such assessments. Current levels of global warming (Haustein et al. 2017) have already intensified heatwaves, droughts and floods, with many recent events exhibiting evidence of being exacerbated by anthropogenic climate change (e.g., Herring et al. 2018, 2016). Recent improvements in understanding demonstrate that half a degree of additional warming will have further severe impacts (Masson-Delmotte et al. 2018). In the context of this rapid and damaging change, there is a clear need to quantify and address both the losses and damages from impacts we have not adapted to today, as well as to adapt to those that will emerge in the next few decades. To do this, it is essential to understand the impacts of man-made climate change on the scales that climate adaptation decisions are made. Drivers of disasters, ultimately responsible for much loss and damage, are unfolding in an ever-changing socio-economic context, which also alters exposure and vulnerability. While various case studies exist (discussed below), there is to date no comprehensive or comparable database quantifying anthropogenic contributions to climate change loss and damage. We suggest that this needs to change.

We assess the suitability of unpaired image-to-image translation networks for bias correcting data simulated by global atmospheric circulation models. We use the UNIT neural network architecture to map between data from the HadGEM3-A-N216 model and ERA5 reanalysis data in a geographical area centred on the South Asian monsoon, which has well-documented serious biases in this model. The UNIT network corrects cross-variable correlations and spatial structures but creates bias corrections with less extreme values than the target distribution. By combining the UNIT neural network with the classical technique of quantile mapping, we can produce bias corrections that are better than either alone. The UNIT+QM scheme is shown to correct cross-variable correlations, spatial patterns, and all marginal distributions of single variables. The careful correction of such joint distributions is of high importance for compound extremes research.