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The urban heat island (UHI) effect exacerbates near‐surface air temperature (T) extremes in cities, with negative impacts for human health, building energy consumption and infrastructure. Using conventional weather models, it is both difficult and computationally expensive to simulate the complex processes controlling neighbourhood‐scale variation of T. We use machine learning (ML) to bias correct and downscale T predictions made by the Met Office operational regional forecast model (UKV) to 100 m horizontal grid length over London, UK. A set of ML models (random forest, XGBoost, multiplayer perceptron) are trained using citizen weather station observations and UKV variables from eight heatwaves, along with high‐resolution land cover data. The ML models improve the T mean absolute error (MAE) by up to 0.12°C (11%) relative to the UKV. They also improve the UHI diurnal and spatial representation, reducing the UHI profile MAE from 0.64°C (UKV) to 0.15°C. A multiple linear regression performs almost as well as the ML models in terms of T MAE, but cannot match the UHI bias correction performance of the ML models, only reducing the UHI profile MAE to 0.49°C. UKV latent heat flux is found to be the most important predictor of T bias. It is demonstrated that including more heatwaves and observation sites in training would reduce overfitting and improve ML model performance. [ABSTRACT FROM AUTHOR]

Differential reflectivity (ZDR$$ {Z}_{\mathrm{DR}} $$) columns were observed using a Met Office three‐dimensional radar composite. An algorithm for automatic detection of ZDR$$ {Z}_{\mathrm{DR}} $$ columns was developed, based on ZDR≥1.0$$ {Z}_{\mathrm{DR}}\ge 1.0 $$ dB and ZH≥10$$ {Z}_{\mathrm{H}}\ge 10 $$ dBZ. Across three case days, detected ZDR$$ {Z}_{\mathrm{DR}} $$ columns were found to precede severe convection in tracked convective cells with a range of lead times from 0 to 20 min depending on the case day. Requiring maxima above 1.4 dB and 30 dBZ of ZDR$$ {Z}_{\mathrm{DR}} $$ and ZH$$ {Z}_{\mathrm{H}} $$ respectively was an appropriate second condition for all three cases although the skill in the early detection of severe convection varied across case days. Despite the high probability of detections, the high false alarm rate accompanied by low critical success index and data latency limit performance based on the three cases considered in this study. Nevertheless, the ability to detect ZDR$$ {Z}_{\mathrm{DR}} $$ columns in operational radar data with a useful lead time prior to severe convection in certain conditions is a promising development towards advancing nowcasting of severe convection in the United Kingdom. [ABSTRACT FROM AUTHOR]