Evapotranspiration, the movement of water from land and plants into the air, is vital for agriculture, water management, and climate, yet it is hard to measure in cloudy regions like Ireland. In this study, we compared its global data with estimates based on local weather data and found errors in all of them. By correcting these errors and combining the best of each source, we created a more reliable product that improved accuracy and reduced biases across different locations and times of year.

We create China’s first open county-level urban flood dataset (2000–2022) using news media data with the help of deep learning.  The dataset reflects both natural and societal influences and includes 7595 urban flood events across 2051 counties, covering 46 % of China’s land area. It reveals the predominance of summer floods, an upward trend since 2000, and a decline from southeast to northwest. Notably, some highly developed regions show a decrease, likely due to improved flood management.

Large hail devastates crops and property and can injure and kill people and livestock. Hail reports are collected by individual countries, so understanding where and when large hail occurs across Europe is an incomplete undertaking. We use the European Severe Weather Database to evaluate the quality of reports by year and by country since 2000. Despite its short record, the dataset appears to represent aspects of European large-hail climatology reliably.

WorldCereal is a global mapping system that addresses food security challenges. It provides seasonal updates on crop areas and irrigation practices, enabling informed decision-making for sustainable agriculture. Our global products offer insights into temporary crop extent, seasonal crop type maps, and seasonal irrigation patterns. WorldCereal is an open-source tool that utilizes space-based technologies, revolutionizing global agricultural mapping.