IndiaWeatherBench: A Dataset and Benchmark for Data-Driven Regional Weather Forecasting over India
This work addresses the problem of inconsistent evaluation in regional weather forecasting for researchers and practitioners, though it is incremental as it builds on existing global forecasting methods.
The authors tackled the lack of standardized benchmarks for regional weather forecasting by introducing IndiaWeatherBench, a comprehensive dataset and evaluation suite for the Indian subcontinent, establishing baselines with various models to enable fair comparisons and reproducibility.
Regional weather forecasting is a critical problem for localized climate adaptation, disaster mitigation, and sustainable development. While machine learning has shown impressive progress in global weather forecasting, regional forecasting remains comparatively underexplored. Existing efforts often use different datasets and experimental setups, limiting fair comparison and reproducibility. We introduce IndiaWeatherBench, a comprehensive benchmark for data-driven regional weather forecasting focused on the Indian subcontinent. IndiaWeatherBench provides a curated dataset built from high-resolution regional reanalysis products, along with a suite of deterministic and probabilistic metrics to facilitate consistent training and evaluation. To establish strong baselines, we implement and evaluate a range of models across diverse architectures, including UNets, Transformers, and Graph-based networks, as well as different boundary conditioning strategies and training objectives. While focused on India, IndiaWeatherBench is easily extensible to other geographic regions. We open-source all raw and preprocessed datasets, model implementations, and evaluation pipelines to promote accessibility and future development. We hope IndiaWeatherBench will serve as a foundation for advancing regional weather forecasting research. Code is available at https://github.com/tung-nd/IndiaWeatherBench.