Agriculture is highly vulnerable to weather variability, including droughts, floods, temperature extremes, and pest outbreaks. Weather-based Decision Support Systems (DSS) integrate meteorological data, weather forecasts, crop models, and expert knowledge to provide timely recommendations for irrigation, sowing, harvesting, nutrient management, and pest control. Advances in artificial intelligence, the Internet of Things, remote sensing, and machine learning have enhanced DSS accuracy and predictive capabilities. This review examines their principles, applications, and role in improving climate resilience, resource-use efficiency, risk management, and agricultural productivity under changing climatic conditions..
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