The JWGFVR aims to advance the development and application of improved diagnostics and verification methods to assess and monitor the quality and value of weather and environmental predictions, for time scales encompassing weather forecasts, sub-seasonal and seasonal predictions, and decadal and climate projections. 

• The JWGFVR engages in the planning and implementation of the verification component of WWRP projects and collaborates with the WCRP Earth System Modelling and Observations (ESMO) core working groups for verification activities within specific WGNE, WGSIP and WGCM projects. The JWGFVR also provides recommendations for the WMO Integrated Processing and Prediction System (WIPPS) verification standards for operational centers. 

• The JWGFVR periodically organizes verification workshops, leads key WWRP and peer-reviewed publications and verification-focused special issues in scientific journals, coordinates international meta-verification inter-comparison projects, to facilitate the further development and advancement of verification research. 

• The JWGFVR periodically organizes verification tutorials, maintains the forecast verification FAQ webpage, and facilitates the development of verification software, to promote good verification practices and facilitate technological transfer from research to operations and services, hence building a professional community in the field. 

• For verification challenges common to several projects (e.g., addressing observation uncertainty and representativeness issue in verification practices), the JWGFVR should serve as catalyst, and take leadership in tackling these fundamental verification research questions, while working in concert -and favoring the communication- between the different projects and working groups involved. 

• In particular, the JWGFVR addresses emerging verification topics arising from the development of data-driven AI/ML models, with a particular focus on aspects such as physical consistency and explainability. By engaging in this novel challenge, the group contributes to shaping robust evaluation strategies that ensure scientific integrity and operational trust in these new forecasting models.