Heatwaves are intensifying worldwide and are among the deadliest weather disasters. The burden falls disproportionately on marginalized populations and the Global South, where under-resourced health systems, exposure to urban heat islands, and the lack of adaptive infrastructure amplify risks. Yet current numerical weather prediction models often fail to capture micro-scale extremes, leaving the most vulnerable excluded from timely early warnings. We present a Graph Neural Network framework for localized, high-resolution temperature forecasting. By leveraging spatial learning and efficient computation, our approach generates forecasts at multiple horizons, up to 48 hours. For Southwestern Ontario, Canada, the model captures temperature patterns with a mean MAE of 1.93$^{\circ}$C across 1-48h forecasts and MAE@48h of 2.93$^{\circ}$C, evaluated using 24h input windows on the largest region. While demonstrated here in a data-rich context, this work lays the foundation for transfer learning approaches that could enable localized, equitable forecasts in data-limited regions of the Global South.

As urbanization and climate change progress, urban heat island effects are becoming more frequent and severe. To formulate effective mitigation plans, cities require detailed air temperature data. However, predictive analytics methods based on conventional machine learning models and limited data infrastructure often provide inaccurate predictions, especially in underserved areas. In this context, geospatial foundation models trained on unstructured global data demonstrate strong generalization and require minimal fine-tuning, offering an alternative for predictions where traditional approaches are limited. This study fine-tunes a geospatial foundation model to predict urban land surface temperatures under future climate scenarios and explores its response to land cover changes using simulated vegetation strategies. The fine-tuned model achieved pixel-wise downscaling errors below 1.74 °C and aligned with ground truth patterns, demonstrating an extrapolation capacity up to 3.62 °C.

The increasingly populated cities of the 21st Century face the challenge of being sustainable and resilient spaces for their inhabitants. However, climate change, among other problems, makes these objectives difficult to achieve. The Urban Heat Island (UHI) phenomenon that occurs in cities, increasing their thermal stress, is one of the stumbling blocks to achieve a more sustainable city. The ability to estimate temperatures with a high degree of accuracy allows for the identification of the highest priority areas in cities where urban improvements need to be made to reduce thermal discomfort. In this work we explore the usefulness of image-to-image deep neural networks (DNNs) for correlating spatial and meteorological variables of a urban area with street-level air temperature. The air temperature at street-level is estimated both spatially and temporally for a specific use case, and compared with existing, well-established numerical models. Based on the obtained results, deep neural networks are confirmed to be faster and less computationally expensive alternative for ground-level air temperature compared to numerical models.

The smallest mention of a heatwave in the UK leads to ice creams selling out, barbecues heating up and shorts being dusted off as the nation celebrates. In June this year, air temperatures in parts of the country soared to over 90 F (33 C), while sharp increases were also felt across Europe, the US and Asia. Air temperatures were recorded in excess of 18 F (10 C) above the average for the time of year in many cities, according to the World Meteorological Organisation. But new heat maps released by the European Space Agency (ESA) show that this might not be such a cause for celebration. They reveal that heat dissipated more slowly in urban areas creating'heat islands' and make life more of a struggle. Experts are worried that this effect will only be exacerbated as climate change continues to take hold.

If human societies do nothing, in just a few decades, the planet could warm to levels it hasn't reached in at least 34 million years, leading to more melting glaciers and floods than ever before -- as well as the dire effect of urban heat waves. In 2021, in the U.S. alone, there were already 18 extreme climate-related disasters with losses exceeding $1 billion each, according to the National Oceanic and Atmospheric Administration. When looking at the world's natural calamities on a consequence and frequency scale, floods and earthquakes have a more devastating effect on people and property, but they occur less frequently than heat waves, which generally take the form of urban heat islands (UHIs). These are also known as heat pockets, which are found across cities' downtown areas, where temperatures are higher than the peripheries. With urbanized areas warming up fast, many more populations globally are bound to face the deadly consequences of the heat-island effect, highlighting urban public health disparities.