After major disasters, public debate often frames them as unexpected or unprecedented events. This reaction is not necessarily due to a lack of warnings. Rather, it reflects how societies process shock and how authorities frequently portray disruption as unavoidable instead of the outcome of earlier institutional choices.
In reality, extreme weather events are rarely unpredictable. Scientists are often able to identify increased risks of storms, floods, droughts, or other hazards days or even weeks in advance. Yet despite these warnings, destructive outcomes continue to occur.
An examination of the catastrophic floods that struck Luxembourg in July 2021, the most damaging disaster in the country’s recorded history, provides insight into why this happens. By reconstructing the scientific forecasts and official responses leading up to the floods, researchers found that the issue was not the absence of warning. Instead, the problem lay in how institutions are structured to respond to risk.
As the United Nations advances its goal of “early warning for all” by 2027, the Luxembourg case highlights a critical lesson: warning systems are often built to act on certainty rather than probability. However, modern forecasting is inherently probabilistic. By the time risks appear definitive enough to trigger formal action, opportunities for meaningful prevention may already have passed.
Nature of Weather Forecasting
Although daily weather forecasts presented on mobile devices often appear definitive, they are fundamentally based on probabilities. Meteorologists run multiple computer simulations to project possible future weather scenarios. The degree to which these simulations align determines the likelihood of hazardous conditions, not guaranteed outcomes.
This probabilistic approach enables forecasters to identify elevated risks well before impacts occur, even if the precise location or magnitude of an event remains uncertain. Paradoxically, uncertainty is typically greatest at longer time horizons, which is precisely when preventive measures would be most effective.
Acting early therefore almost always means acting without full certainty. This is not a flaw in scientific forecasting. It is an unavoidable feature of anticipating complex atmospheric systems in a changing climate. The central challenge lies in whether institutions are prepared to interpret and act upon risk probabilities rather than waiting for confirmation.
Institutional Preference for Certainty
Most early warning systems operate using predefined thresholds. Alert levels, emergency activation protocols, and response plans are triggered only when specific measurable criteria are met. Even when forecasts indicate an increasing likelihood of severe flooding, measures such as evacuations or road closures can typically be implemented only after these formal thresholds are crossed.
Before that point, risk information may circulate within agencies, but it often remains unacted upon. Thresholds serve valuable functions: they clarify decision-making authority, coordinate responses, and prevent unnecessary disruptions. However, they also create a structural bias toward certainty. Action is authorized only when danger is deemed imminent, even if credible evidence of escalating risk already exists.
This dynamic was evident in Luxembourg in July 2021. Forecasts at both European and national levels indicated a high probability of extreme rainfall and flooding up to a week before the disaster occurred. The information was accessible across various components of the warning system. However, uncertainty about precise impacts remained, which is typical in such situations. The decisive factor was how the system managed that uncertainty.
In Luxembourg, response measures were tightly bound to procedural thresholds. As a result, early warnings could not translate into anticipatory action. The national meteorological service and water administration had access to relevant data, but the institutional framework did not empower them to collectively interpret emerging risks or to act before formal criteria were met.
The delay was not primarily due to scientific error or individual operational failure. Meteorological and hydrological services likely operated within the boundaries of their mandates. The choice to wait for formal triggers was institutional rather than technical, reflecting a system designed to prioritize procedural clarity over proactive decision-making.
By the time official action was authorized, many communities had little time to prepare. Evacuations and the installation of flood defenses became significantly more difficult, particularly in areas with limited prior experience of severe flooding. From the perspective of affected residents, warnings appeared to arrive late or not at all, even though risk signals had been present earlier within the system.
Luxembourg’s experience is particularly instructive because it is a small, affluent, and well-connected country. The shortcomings were not rooted in a lack of scientific expertise or financial resources, but in institutional design and societal readiness to act under uncertainty.
The Importance of Learning and Accountability
For early warning systems to improve over time, they must incorporate mechanisms for learning from past failures. This requires transparent and independent evaluations of what worked, what did not, and why.
In several neighboring countries affected by the 2021 floods, including Germany and Belgium, formal inquiries and external reviews were conducted. In Luxembourg, similar processes did not take place.
When expert critique is discouraged or avoided, institutional learning slows. Unresolved questions about system performance leave structural vulnerabilities intact, increasing the likelihood of repeated failures. This reluctance to engage in critical evaluation can itself become a source of systemic risk.
Resilience depends on confronting difficult questions rather than dismissing them as destabilizing. Open analysis strengthens systems by identifying weaknesses and enabling reform.
The risk of extreme weather is increasing across Europe and globally. Early warning systems are central to reducing disaster risk, but their effectiveness hinges on how societies choose to respond to uncertainty.
Uncertainty cannot be eliminated from forecasting. The critical issue is determining how much uncertainty is acceptable when lives, infrastructure, and livelihoods are at stake. Systems designed to delay action until risk becomes nearly certain whether for procedural, organizational, financial, or reputational reasons are more likely to issue warnings that arrive too late to prevent significant harm.
If resilience to future climate risks is to be sustained, early warning systems must evolve. They must be structured not only to generate accurate forecasts but also to interpret probability as sufficient grounds for timely action. Designing institutions that are capable of learning, adapting, and responding earlier to credible risks will be essential in a world where extreme events are becoming more frequent and more severe.