The representation of the Northern Hemisphere (NH) storm tracks and jet streams and their response to climate change have been evaluated in climate model simulations from Phases 3, 5, and 6 of the Coupled Model Intercomparison Project (CMIP3, CMIP5, and CMIP6, respectively). The spatial patterns of the multimodel biases in CMIP3, CMIP5, and CMIP6 are similar; however, the magnitudes of the biases in the CMIP6 models are substantially lower. For instance, the multimodel mean RMSE of the North Atlantic storm track for the CMIP6 models (as measured by time‐filtered sea‐level pressure variance) is over 50% smaller than that of the CMIP3 models in both winter and summer, and over 40% smaller for the North Pacific. The magnitude of the jet stream biases is also reduced in CMIP6, but by a lesser extent. Despite this improved representation of the current climate, the spatial patterns of the climate change response of the NH storm tracks and jet streams remain similar in the CMIP3, CMIP5, and CMIP6 models. The SSP2‐4.5 scenario responses in the CMIP6 models are substantially larger than in the RCP4.5 CMIP5 models, which is consistent with the larger climate sensitivities of the CMIP6 models compared to CMIP5.

There has been huge recent interest in the potential of making operational weather forecasts using machine learning techniques. As they become a part of the weather forecasting toolbox, there is a pressing need to understand how well current machine learning models can simulate high-impact weather events. We compare short to medium-range forecasts of Storm Ciarán, a European windstorm that caused sixteen deaths and extensive damage in Northern Europe, made by machine learning and numerical weather prediction models. The four machine learning models considered (FourCastNet, Pangu-Weather, GraphCast and FourCastNet-v2) produce forecasts that accurately capture the synoptic-scale structure of the cyclone including the position of the cloud head, shape of the warm sector and location of the warm conveyor belt jet, and the large-scale dynamical drivers important for the rapid storm development such as the position of the storm relative to the upper-level jet exit. However, their ability to resolve the more detailed structures important for issuing weather warnings is more mixed. All of the machine learning models underestimate the peak amplitude of winds associated with the storm, only some machine learning models resolve the warm core seclusion and none of the machine learning models capture the sharp bent-back warm frontal gradient. Our study shows there is a great deal about the performance and properties of machine learning weather forecasts that can be derived from case studies of high-impact weather events such as Storm Ciarán.

The Eurasian subtropical westerly jet (ESWJ) is a major feature of the summertime atmospheric circulation in the Northern Hemisphere. Here, we demonstrate a robust weakening trend in the summer ESWJ over the last four decades, linked to significant impacts on extreme weather. Analysis of climate model simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) suggests that anthropogenic aerosols were likely the primary driver of the weakening ESWJ. Warming over mid-high latitudes due to aerosol reductions in Europe, and cooling in the tropics and subtropics due to aerosol increases over South and East Asia acted to reduce the meridional temperature gradient at the surface and in the lower and middle troposphere, leading to reduced vertical shear of the zonal wind and a weaker ESWJ in the upper troposphere. If, as expected, Asian anthropogenic aerosol precursor emissions decline in future, our results imply a renewed strengthening of the summer ESWJ.