Tips & Tricks for a successful HORIZON-CL5-2026-07-D1-05 proposal
Opening
18 December 2025
Deadline
Keywords
climate neutrality
weather models
Climate changes
African communities
Sustainable Energy
Earth System
African dynamics
rainfall
extreme weather events
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HORIZON-CL5-2026-07-D1-05 Horizon Europe call
It is a topic for funding Research and Innovation Actions (RIA) in Horizon Europe Cluster 5 – Climate, Energy and Mobility (Single stage – 2026) aimed at improving climate and weather models for Africa, by enhancing observations, modelling capabilities, and African-European co-created climate science to better predict rainfall, extremes, and climate impacts.
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What do we know about the HORIZON-CL5-2026-07-D1-05 call?
What is the call and where does this topic sit?
- Programme / Cluster: Horizon Europe – Cluster 5 (Climate, Energy and Mobility)
- Destination: Climate sciences and responses for the transformation towards climate neutrality
- Topic: HORIZON-CL5-2026-07-D1-05 – Improving climate and weather models for Africa
- Type of Action: RIA (Research and Innovation Action)
- Opening date: 18 Dec 2025
- Deadline: 15 Apr 2026 17:00:00 Brussels local time.
What budget, and how many projects?
- Topic budget: EUR 21.00 million
- Indicative number of projects funded: 3
- Expected EU contribution per project: around EUR 7.00 million
- Overall indicative budget (for the listed 2026-07 D1 topics together): EUR 82.00 million
What are the results expected by the EU?
The results of the Work Programme are anticipated to enable Africa to be more prepared and able to react to climate changes, and support African science, such as better representation in international climate bodies and programs:
- Improved weather forecasts and climate predictions to assist African communities in reacting to the effects of climate as part of the AU-EU Partnership on Climate Change and Sustainable Energy.
- Greater African climate science capacity, building capacity to nurture the future generation of climate scientists, lessening external expertise dependence and increasing African representation on the international stage, such as IPCC and UNFCCC, and programs such as CMIP, CORDEX, ISIMIP.
What is the scientific scope of HORIZON-CL5-2026-07-D1-05?
Why this topic now?
One of the worst hit areas of climatic change is Africa where vulnerability is high and adaptive capacity low; the world has gone high on development, yet their development gains have not been fully utilized in Africa.
What is the specific matter of the topic asking you to do?
They ought to address crucial knowledge gaps to enhance weather and climate modelling for Africa in a changing climate, and to place emphasis on the understanding and representation of African dynamics at the regional, interregional, and continental scales.
The key scientific/technical expectations are:
-Predict more and forecast less: especially rainfall, extreme weather events.
-Use the observational data to develop process-based knowledge and model the performance of the Earth System components (land, ocean, atmosphere).
-Enhance data collection, assimilation, and quality control and combine local data and rescued historical data as an input to models with high-quality inputs.
-Use digital technologies and AI / ML and improvements in high-performance computing to achieve the objectives of the topic.
What is the model of collaboration the EU desires?
-The African and European teams should conduct research in close collaboration and co-creation to guarantee relevancy, acceptance, and co-ownership by the African stakeholders.
-It is believed that the projects will help in the local building of capacity and dissemination of knowledge, which will enable the African researchers to have data, tools, and knowledge to continue with the development.
-Close relationships are to be developed with the concerned institutions (e.g., universities, research bodies, etc.).
What can you do to maximize the chances of success in the science and tech material?
What can you do to make “improved models for Africa” concrete (and have Excellence score)?
- Put rainfall and extremes front and centre:
-Ensure that your goals are quantifiable (e.g. skill improvements, reduced uncertainty, skill to represent extremes) and tied to African decision needs.
- Develop a plausible chain of investigation – process knowledge procedure enhancement:
-Demand demonstration on how observations are going to be converted to parameterizations, data assimilation upgrades, bias correction, or evaluation protocols.
- Make data rescue + QA + assimilation an administrative job, not a science:
-The scope itself expressly appreciates both the local and rescued historical data, and the quality assurance and assimilation practices- use them as a foundation WP.
- AI/ML should be used in the area it deserves:
-Position AI/ML and make it look as one can be done with emulators (emulators enable us to do it), downscaling (it can be done with downscaling as well), QC automation, hybrid modelling (with downscaling, we can do this), and how you prevent the black-box-only credibility problems.
- Africa-ready operationalisation:
-Include demonstrations of avenues to uptake, even in cases where you are not providing an operational service (national met services, regional centres, climate services communities).
What are your ways of de-risking the hardest scientific arguments?
- Be explicit about:
-Sparse/heterogeneous observations
-Inconsistencies in convection/rainfall.
-Model bias and interregional/seasonal generalisation.
-HPC limitations and reproducibility.
- Convert them into:
-Decision milestones, go/no-go points, fallback options (alternate data sources, alternate modelling approaches).
What do you increase the chances of success in writing consortium and proposals?
What RIA shape is more common in climate modelling of a EUR -7M?
A balanced mix is usually the best:
-Universities / research institutes (with actual scientific leadership roles) in Africa.
-African national meteorological and hydrological services or regional climate centres (for relevance + uptake)
-European modelling centres/universities (methodological depth)
-HPC/data experts (data pipelines, scalability, reproducibility)
-To demonstrate relevance, end-user organisations (risk management, agriculture, water, health interfaces) are required.
-Add some innovative SME (e.g., data engineering, AI/ML tooling, sensor systems, digital platforms) to enhance exploitation capacity.
What writing movements are consistent with proposal evaluation?
Proposals are rated by the evaluators against Excellence, Impact, and Quality and efficiency of implementation (standard EU practice).
Workable propositions that are always useful:
- Reflect the subject matter wording (in a polite but not mechanical manner):
Utilize the scope language: knowledge gaps, rainfall/extremes, observational data, QA/assimilation, AI/ML, HPC, co-creation, capacity-building.
- Construct an Africa-specific “Pathway to impact”:
Who uses improved predictions? When? Through which channels? What changes because of your project?
- Make it inevitable: Implement it.
Clarity of the WPs, plausibility of person-months, plausible data management, definition of risks and mitigations (the evaluation form clearly considers the quality of work plans and risk evaluation).
- Keep the co-ownership promise visible throughout:
Assign African partners to leadership (WP leads, task leads, publications strategy, data governance) due to the clearly anticipated co-creation.
Which details can be considered as small and enhance the experience of the evaluator?
- One-page “Project logic map”: Input (data/HPC)-methodologies-improvement of the model-validation-products-capacity-building-uptake.
- A relatively brief, focused capacity-building plan: Integrating training modules, exchanges, joint supervision, reproducible workflow handed over, community building.
- Reproducibility as a differentiator: annotated datasets, recorded pipelines, subject to notebook recipes, articulate QA procedures.
What is the possible use of microfluidics in this topic?
Microfluidics could play a supportive, yet mighty, role through enhancing the quality and availability of observational data, which the topic explicably appreciates (data collection, QA, assimilation, including local data and rescued historical data).
The microfluidics entry points are realistic:
- Small-scale, field-deployable, lab-on-chip-scale sensing modules that will permit more dense observation networks in data low-density areas: Environmental chemistry proxy, water quality/contaminants related to hydrology or sampling workflows that are calibration-friendly.
- Sample manipulation and control: Microfluidic systems may include standard pre-processing (filtration, concentration, multiplexing), thereby reducing much of the variability before data assimilation.
- Research support (where appropriate): Aerosols/cloud physics research support. The microfluidic platforms can be used to investigate droplet formation or particle-fluid interactions under controlled conditions, which can aid in understanding processes that ultimately enhance parameterisations.
- Capacity-building hardware: training. Basic, rugged microfluidic teaching packages can afford local competence-building in the context of measurement, QA, and instrumentation, consistent with the capacity-building anticipation of the topic.
The MIC already brings its expertise in microfluidics to Horizon Europe:
H2020-NMBP-TR-IND-2020
Microfluidic platform to study the interaction of cancer cells with lymphatic tissue
H2020-LC-GD-2020-3
Toxicology assessment of pharmaceutical products on a placenta-on-chip model
