When floods devastated Nigeria in 2022, more than a million people were displaced, farmland was submerged, and essential infrastructure collapsed. It was a stark reminder that climate change is a present danger. Rising seas threaten Lagos, desertification is eroding livelihoods in the north, and erratic rainfall is destabilising agriculture.

My recent co-authored scholarly publication titled ‘Machine learning and morphometric analysis for runoff dynamics: Enhancing flood management and catchment prioritisation in Bayelsa, Nigeria’ and ‘Spatial evaluation of flood risk using geospatial and multi-criteria decision analysis (MCDA): A case study in Obio Akpor, Rivers State, Nigeria’ tells me that the scale of these challenges requires new tools that can anticipate risks and guide action.

And no better technology tools than Artificial Intelligence (AI) and machine learning (ML) can suffice in this regard. AI is emerging globally as one of the most powerful resources for climate adaptation. Countries such as the United Kingdom and the United States are showing how AI can be applied at scale. Nigeria must now adapt these global models to its own context.

The UK Met Office, supported by the Alan Turing Institute, uses advanced climate simulations to provide detailed forecasts of flooding, heatwaves, and long-term risks. Crucially, these forecasts are not confined to academic reports. They shape planning decisions for local councils, influence insurance markets, and inform national adaptation strategies.

The message for Nigeria is clear: predictive tools must not remain in technical silos. If AI systems can model rainfall variability in Lokoja or forecast river surges in Bayelsa, their outputs should inform evacuation strategies, building regulations, and agricultural planning. To achieve this, Nigeria needs strong institutional linkages and mentorship structures so that researchers can work directly with government actors to translate models into action.

The US offers another lesson through its emphasis on partnerships. NASA and the National Oceanic and Atmospheric Administration (NOAA) collaborate widely with universities and the private sector to scale AI models. The SERVIR programme, managed by NASA and USAID, provides satellite-driven climate services across Africa, Asia, and Latin America. What makes SERVIR distinctive is not just its technology but its training of local scientists to adapt and apply the tools themselves. This combination of knowledge transfer and capacity-building is what ensures sustainability.

Nigeria could develop a similar framework. A collaboration between the Nigerian Meteorological Agency, universities, and local technology firms, supported by international partners, could deliver AI-based tools for flood forecasting and agricultural planning while also mentoring young Nigerian scientists to maintain and expand the systems.

The case for localisation is strong. Nigeria’s environmental realities differ significantly from those of Europe or North America. Informal housing in flood-prone areas, dependence on rain-fed agriculture, and inadequate drainage systems are conditions that imported models often miss.

When we worked on environmental modelling projects, we found that global datasets alone could not capture these complexities. By incorporating local borehole records and vegetation indices alongside satellite data, we achieved far more reliable results for communities in the Niger Delta. This experience underscored two lessons: first, that Nigerian data must be central to Nigerian models; and second, that mentorship is indispensable. Training students not only improved their technical skills but also showed them how research can be applied to community needs. This is precisely the kind of mentorship ecosystem that Nigeria must scale up.

Other Nigerian experts have reached similar conclusions. Writing in a recent The Guardian piece titled ‘What Mokwa flooding should teach Nigeria about climate technology policy’, climate and data scientist, Ugochukwu Charles Akajiaku, stressed that Nigeria needs to adopt real-time climate monitoring systems powered by AI and machine learning to improve disaster preparedness.

Petroleum engineer and geospatial analyst, Meremu Dogiye Amos, in her The Guardian intervention titled ‘FG’s reactive response to climate, environmental problems not good for Nigeria’ has also argued that reactive response to flooding and environmental challenges is unsustainable, warning that without proactive, technology-driven strategies, the country will remain vulnerable.

Furthermore, AI researcher Okes Imoni used her Guardian opinion article titled ‘What Nigeria can learn from UK, US climate AI models’ to call for the adaptation of UK and US climate-AI frameworks in Nigeria, emphasising the role of mentorship and local capacity-building.

These contributions show that there is already a strong Nigerian voice in the global conversation on climate technology. The challenge is to connect these insights with policy and practice. Mentorship is the unifying thread. In both the UK and the US, mentorship is not an afterthought but part of how climate research and policy are structured.

Nigeria’s AI and climate projects should be designed the same way. Each initiative should serve as both a research platform and a training opportunity. For instance, a project forecasting crop yields should not only produce data but also include workshops for students, extension workers, and local officials on how to apply the findings. Each project can therefore multiply its impact, producing knowledge while expanding the pool of people able to apply it.

Another priority is communication. Sophisticated models are of little value if their outputs are inaccessible. The Met Office produces user-friendly climate risk reports that councils and businesses can act upon. SERVIR, in turn, codesigns its tools with stakeholders so they address real-world needs.

Nigeria should follow this example. Rainfall forecasts must be communicated to farmers in ways that support planting and harvesting decisions. Urban governments need clear dashboards connecting climate projections to drainage and housing policies. Translating science into action is itself a form of mentorship, requiring technical experts to guide non-specialists in applying AI insights.

Nigeria does face barriers — limited high-performance computing, fragmented data systems, and low levels of research investment. Yet these should not become excuses for inaction. What the UK and US models show is that climate AI is as much about governance, collaboration, and human capacity as it is about hardware. Nigeria has the human capital — what is missing is the institutional framework to channel it.

The costs of delay are heavy. Extreme weather is already depressing agricultural output, straining public health, and forcing communities into displacement. Without adaptation, these pressures will intensify. But AI offers the chance to shift from reactive crisis management to proactive resilience planning. Nigeria has a young generation of scientists, data analysts, and entrepreneurs eager to take on this challenge. They need access to data, supportive mentorship, and meaningful partnerships that connect their work to policymaking.

Nigeria cannot afford to remain a passive consumer of foreign technologies. By adapting global models to its own realities, embedding mentorship into every climate initiative, and ensuring that AI outputs are turned into usable decisions, Nigeria can lead rather than lag.

The insights of experts such as Ugochukwu Charles Akajiaku, Meremu Dogiye Amos, and Okes Imoni remind us that Nigerian voices are already pointing in this direction. What is required now is the political will to act on them.

Climate change is accelerating, and its impacts will not wait. But with deliberate actions, learning from the UK and the US, localising solutions, and investing in mentorship, Nigeria can turn vulnerability into resilience.

The choice is clear — to remain reactive in the face of disaster or to build the systems today that will safeguard tomorrow.

 Winston, a data scientist postgraduate student, writes from Coventry University, United Kingdom