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Abstract:

We assess near-surface temperature and sea surface temperature trends in 8 seasonal forecast systems in the Copernicus Climate Change Service archive, over the common hindcast period (1993–2016). All but one of the systems show a faster warming of the global-mean, relative to observations in both boreal summer and winter seasons. On average, systems warm at 0.21K/decade and 0.22K/decade for winter and summer, respectively, compared to 0.17K/decade and 0.19K/decade for ERA5. In summer, forecast systems tend to show an excessive warming of the tropical Pacific, tropical Atlantic and southern mid-latitudes, which contributes to the difference in global warming rates compared to observations. In contrast, greater warming in the northern mid-latitudes contributes most to trend differences for winter. The faster warming of models over this period has important implications for seasonal forecasts of future global and regional temperature and suggests further work is required to understand this bias.

Abstract:

While it is widely believed that the intense rainfall in summer 2022 over Pakistan was substantially exacerbated by anthropogenic climate change1, 2, climate models struggled to confirm this3, 4. Using a high-resolution operational seasonal forecasting system that successfully predicted the extreme wet conditions, we perform counterfactual experiments simulating pre-industrial and future conditions. Both experiments also exhibit strong anomalous rainfall, indicating a limited role of CO2-induced forcing. We attribute 10% of the total rainfall to historical increases in CO2 and ocean temperature. However, further increases in the future suggest a weak mean precipitation reduction but with increased variability. By decomposing rainfall and large-scale circulation into CO2 and SST-related signals, we illustrate a tendency for these signals to compensate each other in future scenarios. This suggests that historical CO2 impacts may not reliably predict future responses. Accurately capturing local dynamics is therefore essential for regional climate adaptation planning and for informing loss and damage discussions.

Abstract:

<jats:title>Abstract</jats:title> <jats:p>Flash droughts—characterized by their rapid onset—can cause devastating socioeconomic and agricultural damage. During such events, soil moisture depletion is driven not only by precipitation shortages but also by the elevated atmospheric moisture demand arising due to extreme heat. However, the role of extreme heat in shaping the evolution of flash droughts and their ecological impacts remains uncertain. Here we investigate the processes involved by analysing global reanalysis data from 1950 to 2022. We find that, when flash droughts are accompanied by extreme heat, they exhibit 6.7–90.8% higher severity and 8.3–114.3% longer recovery time than flash droughts without extreme heat. The presence of extreme heat during flash droughts accelerates soil moisture drawdown over high latitudes, where wet soils and enhanced radiation foster evapotranspiration. By contrast, it slows the absolute onset speed in subtropical transitional climate zones owing to evapotranspiration throttling. Our machine learning approach further reveals that hot flash droughts lead to sharper declines in ecosystem productivity, particularly in croplands, thereby threatening global food security. These findings underscore the pressing need for enhanced infrastructure and ecosystem resilience to hot flash droughts in a warming future.</jats:p>