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Accurate long‐term flood predictions are increasingly needed for flood risk management in a changing climate, but are hindered by the underestimation of climate variability by climate models. Here, we drive a statistical flood model with a large ensemble of dynamical CMIP5‐6 predictions of precipitation and temperature. Predictions of UK winter flooding (95th streamflow percentile) have low skill when using the raw 676‐member ensemble averaged over lead times of 2–5 years from the initialization date. Sub‐selecting 20 ensemble members that adequately represent the multiyear temporal variability in the North Atlantic Oscillation (NAO) significantly improves the flood predictions. Applying this method we show positive skill in 46% of stations compared to 26% using the raw ensemble, primarily in regions most strongly influenced by the NAO. Our findings reveal the potential of decadal predictions to inform flood risk management at long lead times. Plain Language Summary: Reliable predictions of flooding can help society to manage the associated risk to lives and property. Seasonal predictions of flooding over the coming months already form the basis of many operational services around the world. In contrast, decadal predictions with lead times of up to 10 years are more challenging, due to the difficulty of simulating dynamic changes in atmospheric circulation at these timescales. Here, we show that a large ensemble of climate models can predict average winter flood conditions over the UK in the next decade. The climate models underestimate the magnitude of atmospheric variability in the north Atlantic and identifying a subset of skillful climate model simulations improves the ability to predict floods. Our results suggest that decadal climate predictions may be useful in the context of flood risk management. However, the use of multiyear averages for flood prediction is still poorly studied, and therefore further work should help determine how such predictions can be used in an operational setting. Key Points: Multiyear predictions of mean winter floods 2–5 years ahead are skillful across much of the UKSkill is improved by "NAO‐matching" to overcome spuriously weak modeled signalsThe higher the sensitivity of streamflow to the North Atlantic Oscillation at a given gauge, the greater the benefit of NAO‐matching for decadal flood prediction [ABSTRACT FROM AUTHOR]

We demonstrate a 1-year lagged extratropical response to the El Niño-Southern Oscillation (ENSO) in observational analyses and climate models. The response maps onto the Arctic Oscillation and is strongest in the North Atlantic, where it resembles the North Atlantic Oscillation (NAO). Unexpectedly, these 1-year lagged teleconnections are at least as strong as the better-known simultaneous winter connections. However, the 1-year lagged response is oppositein sign to the simultaneous response such that 1 year later, El Niño is followed by a positive NAO, whereas La Niña is followed by a negative NAO. The lagged response may also interfere with simultaneous ENSO teleconnections. We show here that these effects are unlikely to be caused by residual aliasing of ENSO cycles; rather, slowly migrating atmospheric angular momentum anomalies explain both the sign and the timing of the extratropical response. Our results have implications for understanding ENSO teleconnections, explaining observed extratropical climate variability and interpreting seasonal to interannual climate predictions.

Skilful predictions of near-term climate extremes are key to a resilient society. However, standard methods of analysing seasonal forecasts are not optimised to identify the rarer and most impactful extremes. For example, standard tercile probability maps, used in real-time regional climate outlooks, failed to convey the extreme magnitude of summer 2022 Pakistan rainfall that was, in fact, widely predicted by seasonal forecasts. Here we argue that, in this case, a strong summer La Niña provided a window of opportunity to issue a much more confident forecast for extreme rainfall than average skill estimates would suggest. We explore ways of building forecast confidence via a physical understanding of dynamical mechanisms, perturbation experiments to isolate extreme drivers, and simple empirical relationships. We highlight the need for more detailed routine monitoring of forecasts, with improved tools, to identify regional climate extremes and hence utilise windows of opportunity to issue trustworthy and actionable early warnings., (© 2023. Springer Nature Limited.)

Myzus persicae (Sulzer, Hemiptera: Aphididae) is a major global crop pest; it is the primary aphid vector for many damaging viruses and has developed resistance to most insecticides. In temperate regions, the risk of widespread crop infection and yield loss is heightened following warm winters, which encourage rapid population growth and early flight. Estimates of the frequency and magnitude of warm winters are, therefore, helpful for understanding and managing this risk. However, it is difficult to quantify the statistical distribution of climate events, particularly extremes, because climate observations represent just a small sample of the possible climate variations in a region. The purpose of this study was to establish a large-scale relationship between temperature and M. persicae observations across the UK and apply this to a very large ensemble of climate model simulations, which better sample the variability in climate, to quantify the current likelihood of extreme early M. persicae flight across the UK. The timing of M. persicae flight was shown to be significantly related to January-February mean temperature, where a 1°C warmer/cooler temperature relates to about 12 d earlier/later flight. Climate model simulations predict 40% likelihood of experiencing a year with unprecedented early M. persicae flight during the next decade in the UK. Results from this method can help crop managers assess the long-term viability of crops and management practices across the UK and provide early warning information for targeting pest surveillance activities on the locations and timings at highest risk of early M. persicae flight., (© Crown copyright 2022.)