Spanish data science SME specialising in climate time series analysis, sub-seasonal weather forecasting, and extreme events statistics.
Predictia is a Spanish data science SME specialising in climate and weather analytics. They build tools and methods for processing complex environmental time series, extracting statistical patterns from climate model outputs, and characterising the behaviour of extreme weather events. In EU research projects they function as a specialist data partner — contributing analytical software and statistical expertise to large international consortia rather than leading projects themselves. Their positioning at the intersection of data engineering and climate science makes them a practical bridge between raw model output and decision-relevant information.
Contributed to both PRIMAVERA (high-resolution climate simulation and European climate risk) and CAFE (sub-seasonal climate forecasting), spanning the full range of climate data work from long-run simulation to near-term prediction.
CAFE (2019–2023) explicitly targets sub-seasonal extreme weather forecasting, with Predictia contributing keywords around sub-seasonal predictability and coherent atmospheric structures.
CAFE project keywords include extreme events statistics and weather patterns, indicating a specific methodological capability in quantifying the frequency and intensity of climate extremes.
Time series analysis appears as a named keyword from CAFE, consistent with Predictia's stated identity as an intelligent data solutions firm applying statistical methods to sequential environmental data.
In their first H2020 project (PRIMAVERA, starting 2015) Predictia was embedded in a broad climate simulation effort focused on high-resolution modelling and long-term European climate risk — an area driven by model development rather than operational forecasting. By the time they joined CAFE (2019), their contribution had narrowed and sharpened to sub-seasonal predictability, extreme event statistics, and the detection of coherent atmospheric structures — a clearly more applied and decision-oriented domain. The shift indicates a deliberate move from supporting large modelling infrastructure toward delivering near-term, high-impact forecast intelligence.
Predictia is moving toward operationally-relevant short-range climate services, suggesting future collaborations in climate services, early-warning systems, and climate risk products for industry or public authorities.
Predictia has never coordinated an H2020 project, always entering as a participant or third-party specialist. They work comfortably inside very large consortia — both projects they joined carried broad international partnerships — which shows they are experienced at defining a contained technical role within complex multi-partner structures. This makes them a low-friction partner to bring in for specific data analysis tasks without requiring them to take on project management responsibility.
Despite only two projects, Predictia has built a surprisingly wide network of 36 unique consortium partners across 10 countries. This breadth relative to project count reflects the large size of the consortia they joined (PRIMAVERA alone had over 20 partners), giving them exposure to leading European climate research institutions well beyond what their funding volume suggests.
Predictia occupies a rare niche as a private SME with deep climate-data expertise in a field dominated by public research institutes and universities. For a consortium builder, this means they bring commercial agility and software delivery discipline alongside scientific credibility — a combination that strengthens real-world application work packages. Their Santander base also connects them to the broader Spanish climate and ocean research community, including IFCA and related CSIC institutes.
