Weather‐pattern, or weather‐type, classifications are a valuable tool in many applications as they characterize the broad‐scale atmospheric circulation over a given region. This study analyses the aspects of regional UK precipitation and meteorological drought climatology with respect to a new set of objectively defined weather patterns. These new patterns are currently being used by the Met Office in several probabilistic forecasting applications driven by ensemble forecasting systems. Weather pattern definitions and daily occurrences are mapped to Lamb weather types (LWTs), and parallels between the two classifications are drawn. Daily precipitation distributions are associated with each weather pattern and LWT. Standardized precipitation index (SPI) and drought severity index (DSI) series are calculated for a range of aggregation periods and seasons. Monthly weather‐pattern frequency anomalies are calculated for SPI wet and dry periods and for the 5% most intense DSI‐based drought months. The new weather‐pattern definitions and daily occurrences largely agree with their respective LWTs, allowing comparison between the two classifications. There is also broad agreement between weather pattern and LWT changes in frequencies. The new data set is shown to be adequate for precipitation‐based analyses in the UK, although a smaller set of clustered weather patterns is not. Furthermore, intra‐pattern precipitation variability is lower in the new classification compared to the LWTs, which is an advantage in this context. Six of the new weather patterns are associated with drought over the entire UK, with several other patterns linked to regional drought. It is demonstrated that the new data set of weather patterns offers a new opportunity for classification‐based analyses in the UK.

Wildfire can cause significant adverse impacts to society and the environment. Weather and climate play an important role in modulating wildfire activity. We explore the joint occurrence of global fire weather and meteorological drought using a compound events framework. We show that, for much of the globe, burned area increases when periods of heightened fire weather compound with dry antecedent conditions. Regions associated with wildfire disasters, such as southern Australia and the western USA, are prone to experiencing years of compound drought and fire weather. Such compound events have increased in frequency for much of the globe, driven primarily by increases in fire weather rather than changes in precipitation. El Ni$$\tilde{{{{\rm{n}}}}}$$ n ̃ o Southern Oscillation is associated with widespread, spatially compounding drought and fire weather. In the Northern Hemisphere, a La Ni$$\tilde{{{{\rm{n}}}}}$$ n ̃ a signature is evident, whereas El Ni$$\tilde{{{{\rm{n}}}}}$$ n ̃ o is associated with such events in the tropics and, to a lesser degree, the Southern Hemisphere. Other climate modes and regional patterns of atmospheric circulation are also important, depending on the region. We show that the lengths of the fire weather seasons in eastern Australia and western North America have increased substantially since 2000, raising the likelihood of overlapping fire weather events in these regions. These cross-hemispheric events may be linked to the occurrence of El Ni$$\tilde{{{{\rm{n}}}}}$$ n ̃ o, although the sea-surface temperature magnitudes are small. Instead, it is likely that anthropogenic climate change is the primary driver of these changes.

Medium‐ to long‐range precipitation forecasts are a crucial component in mitigating the impacts of fluvial flood events. Although precipitation is difficult to predict at these lead times, the forecast skill of atmospheric circulation tends to be greater. The study explores using weather patterns (WPs) as a preliminary step in producing forecasts of upper‐tail precipitation threshold exceedance probabilities for the UK. The WPs are predefined, discrete states representing daily mean sea‐level pressure (MSLP) over a European–North Atlantic domain. The WPs most likely to be associated with flooding are highlighted by calculating upper‐tail exceedance probabilities derived from the conditional distributions of regional precipitation given each WP. WPs associated with higher probabilities of extreme precipitation are shown to have occurred during two well‐known flood events: the 2014 Somerset Levels floods in southwest England; and Storm Desmond over the northern UK in December 2015. To illustrate the potential of this WP‐based prediction framework, a forecast guidance tool called Fluvial Decider is introduced. It is intended for use by hydro‐meteorologists in the England and Wales Flood Forecasting Centre (FFC). Forecasts of the MSLP from ensemble prediction systems (EPSs) are assigned to the closest‐matching WP, providing daily probabilistic forecasts of WPs out to the chosen lead time. Combining these probabilities with observed precipitation threshold exceedance probabilities provides a parsimonious tool for highlighting potential periods with increased risk of flooding. Model forecasts using the European Centre for Medium‐range Weather Forecasts (ECMWF) EPS highlighted both flood events as being at a higher than average risk of heavy extreme precipitation at lead times of over five days.

Assessments of climate forecast skill depend on choices made by the assessor. In this perspective, we use forecasts of the El Niño-Southern-Oscillation to outline the impact of bias-correction on skill. Many assessments of skill from hindcasts (past forecasts) are probably overestimates of attainable forecast skill because the hindcasts are informed by observations over the period assessed that would not be available to real forecasts. Differences between hindcast and forecast skill result from changes in model biases from the period used to form forecast anomalies to the period over which the forecast is made. The relative skill rankings of models can change between hindcast and forecast systems because different models have different changes in bias across periods.