  Advanced weather and climate models use numerical techniques on grided meshes
to simulate atmospheric and ocean dynamics, which are computationally
expensive. Data-driven approaches are gaining popularity in weather and climate
modeling, with a broad scope of applications. Although Machine Learning (ML)
has been employed in this domain, significant progress has occurred in the past
decade, leading to ML applications that are now competitive with traditional
numerical methods. This study presents a user-friendly dataset for data-driven
medium-range weather forecasting focused on India. The dataset is derived from
IMDAA reanalysis datasets and optimized for ML applications. The study provides
clear evaluation metrics and a few baseline scores from simple linear
regression techniques and deep learning models. The dataset can be found at
https://www.kaggle.com/datasets/maslab/bharatbench, while the codes are
available at https://github.com/MASLABnitrkl/BharatBench. We hope this dataset
will boost data-driven weather forecasting over India. We also address
limitations in the current evaluation process and future challenges in
data-driven weather forecasting.
