Weather and climate services (W&CS) are key to supporting the transition to net-zero emissions in the energy sector. These services are utilized to increase energy system resilience, enhance renewable energy deployment, and enable the uptake of energy-efficiency measures and innovations. As energy systems become increasingly dependent on and affected by weather and climatic conditions, integrating weather and climate data into energy management systems is essential. This paper addresses the gap in comprehensive guidance for developing integrated W&CS to support net-zero energy transitions, drawing upon a report by the World Meteorological Organization’s Services Commission Study Group on Integrated Energy Services. We present a framework for coproduction of W&CS, exploring how the uptake of W&CS for energy transitions can be enabled through the evaluation of socioeconomic benefits, harnessing business models, identification of key policies, and capacity development measures. To support the uptake of W&CS for net-zero energy transitions, we recommend a deeper understanding of user needs and requirements; continuous advancements in the science and technology of W&CS; effective integration of weather and climate data within energy conversion models; improved accessibility and sharing of meteorological and, especially, energy data; promotion of coproduction approaches; exploration of novel applications of W&CS in the energy sector; refinement of business models for sustainable W&CS delivery; execution of capacity-building activities; and enhanced communication among stakeholders and strengthened collaborative efforts. These steps are crucial for realizing the full potential of W&CS in driving the energy sector toward a sustainable, net-zero future. Significance Statement: Weather and climate services can support energy-systems’ resilience and acceleration of their decarbonization, particularly considering growing uncertainties regarding weather and climate variability. Still, these services remain underutilized and comprehensive guidance on their deployment is lacking. Here, we present a framework for coproduction of weather and climate services in the energy sector. We argue that involving relevant stakeholders at all stages of coproduction may increase these services’ credibility, usefulness, and uptake. We find that understanding weather and climate services’ value, enabling sustainable business models, supportive policies, and capacity development measures, may enable their uptake. We then put forward recommendations to support the uptake of weather and climate services for a net-zero energy transition among various actors and institutions.

The importance of energy transition was underlined at COP28 in Dubai, where governments committed to tripling renewables capacities and doubling the rate of energy efficiency by 2030. However, the power generated by climate-dependent energy sources exhibits greater vulnerability to potential climate changes in the long term. Therefore, climate models play a pivotal role in estimating the effects of climate change on renewables in the context of strategic planning for the development and operation of new renewable power plants. In this context, the World Meteorological Organization (WMO) developed a Global Energy Resilience Atlas aimed at providing insights into the climate change risks for the hydropower sector, the largest renewable electricity source for most countries, generating over 4300 TWh globally. This study focuses on defining four Hydro Climate Risk indices ($\mathrm{H}\mathrm{C}\mathrm{R}\mathrm{I}\mathrm{s}$) using historical and climate projection precipitation data for three climate scenarios. The final product is a freely available and interactive tool. The developed methodology and tool address how climate changes have historically affected hydropower generation and how they will impact the future at national scales. The final product also addresses the needs of policymakers at national, regional, and global levels in crafting long-term planning for a more secure energy sector, accelerating the energy transition to more sustainable and reliable energies.

The energy sector relies on numerical model output forecasts for operational purposes on a short-term scale, up to 10 days ahead. Reducing model errors is crucial, particularly given that coarse resolution models often fail to account for complex topography, such as that found in Costa Rica. Local circulations affect wind conditions at the level of wind turbines, thereby impacting wind energy production. This work addresses a specific need of the Costa Rican Institute of Electricity (ICE) as a public service provider for the energy sector. The developed methodology and implemented product in this study serves as a proof of concept that could be replicated by WMO members. It demonstrates a product for wind speed forecasting at wind power plants by employing a novel strategy for model input selection based on large-scale indicators leveraging artificial intelligence-based forecasting methods. The product is developed and implemented based on the full-value chain framework for weather, water, and climate services for the energy sector introduced by the WMO. The results indicate a reduction in the wind forecast RMSE by approximately 55% compared to the GFS grid values. The conclusion is that combining coarse model outputs with regional climatological knowledge through AI-based downscaling models is an effective approach for obtaining reliable local short-term wind forecasts up to 10 days ahead.

The energy sector relies on numerical model output forecasts for operational purposes on a short-term scale, up to 10 days ahead. Reducing model errors is crucial, particularly given that coarse resolution models often fail to account for complex topography, such as that found in Costa Rica. Local circulations affect wind conditions at the level of wind turbines, thereby impacting wind energy production. This work addresses a specific need of the Costa Rican Institute of Electricity (ICE) as a public service provider for the energy sector. The developed and implemented product in this study serves as a proof of concept that could be replicated by WMO Members. It demonstrates a product for wind speed forecasting at wind power plants by employing a novel strategy for input selection based on large-scale indicators to enhance Artificial Intelligence-based forecasting methods. The product is developed and implemented based on the full-value chain framework for weather, water, and climate services for the energy sector introduced by the WMO. The results indicate a reduction of wind forecast RMSE by approximately 55% compared to the GFS grid values. The conclusion is that combining coarse model outputs with regional climatological knowledge through AI-based downscaling models is an effective approach for obtaining reliable local short-term wind forecasts up to 10 days ahead.