Weather and Climate Information for Health Applications: From Forecasts to Early Action

Introduction

When people think about weather forecasts, they often associate them with deciding whether to carry an umbrella, plan a journey, or prepare for a storm. However, weather and climate information serves a much broader purpose. Increasingly, it has become an indispensable tool for protecting public health.

Temperature, rainfall, humidity, wind, and other meteorological variables influence the occurrence and spread of many diseases. Heatwaves can lead to heat exhaustion and heat stroke, prolonged rainfall may trigger outbreaks of water-borne diseases, while warm and humid conditions create favorable environments for mosquitoes that transmit diseases such as dengue and malaria. Droughts, on the other hand, can affect food security, nutrition, and access to safe drinking water.

As climate variability and climate change increase the frequency and intensity of extreme weather events, integrating weather and climate information into health decision-making has become more important than ever.

Climate as a Determinant of Health

Health is influenced not only by medical care but also by environmental conditions. Weather and climate are among the most significant environmental determinants of human health (Figure 1).

Some examples include:

• Heatwaves increasing heat-related illnesses and mortality.
• Heavy rainfall and flooding increasing the risk of water-borne diseases such as cholera and diarrheal infections.
• Temperature and humidity affecting the breeding and survival of mosquitoes responsible for dengue, malaria, and chikungunya.
• Droughts contributing to malnutrition, food insecurity, and reduced water availability.
• Air pollution episodes, influenced by meteorological conditions, aggravating respiratory and cardiovascular diseases.

Understanding these relationships enables health authorities to move from reactive responses to proactive preparedness.

Figure 1. The climate–health nexus: Weather and climate influence human health through multiple pathways, including heatwaves, floods, droughts, and vector-borne diseases.

Seamless Weather and Climate Forecasting for Health

Health decisions require information at different timescales. A seamless forecasting framework provides weather and climate information ranging from hours to months (Figure 2).

• Nowcasting (0–6 hours): Supports emergency response to thunderstorms, lightning, and heavy rainfall.
• Short-Range Forecasts (1–3 days): Assist in issuing heatwave and cold-wave advisories and hospital preparedness.
• Medium-Range Forecasts (4–10 days): Enable planning for heavy rainfall, flooding, and disease surveillance.
• Extended-Range Forecasts (2–4 weeks): Support vector-control activities and resource mobilization.
• Seasonal Climate Outlooks (1–6 months): Assist strategic planning for climate-sensitive diseases and health-resource allocation.
• Climate Projections (Years–Decades): Guide long-term adaptation planning and climate-resilient health infrastructure.

Each forecasting horizon supports a different level of preparedness, demonstrating that weather and climate information is valuable far beyond daily weather forecasts.

Figure 2. Seamless forecasting integrates weather and climate prediction systems across timescales—from hours to decades—to support informed health-sector decision-making. Immediate forecasts guide emergency response, extended-range and seasonal outlooks strengthen preparedness for climate-sensitive diseases, while long-term climate projections support adaptation planning and the development of resilient public health systems.

Climate Variability and Disease Risk

Large-scale climate phenomena such as the El Niño–Southern Oscillation (ENSO) significantly influence temperature and rainfall patterns across many parts of the world, including India.

During El Niño years, many regions experience hotter and drier conditions, increasing the likelihood of heatwaves, drought, dehydration, and food insecurity. In contrast, La Niña events often bring wetter conditions, increasing the risk of flooding, mosquito breeding, and outbreaks of vector-borne and water-borne diseases (Figure 3).

These climate signals provide valuable information months in advance, offering opportunities for seasonal health preparedness.

Figure 3. Illustration of the pathways through which ENSO influences climate-sensitive health risks. By modifying regional temperature, rainfall, and humidity patterns, El Niño and La Niña affect environmental conditions that can alter the risks of heat-related illnesses, vector-borne diseases, water-borne diseases, and food insecurity. Understanding these relationships enables seasonal preparedness and climate-informed public health planning.

Operational Perspectives from IMD

The India Meteorological Department (IMD) has progressively strengthened its weather and climate services that are directly relevant to the health sector.

These include:

• Heatwave monitoring and forecasting
• Impact-based weather warnings
• Extended-range and seasonal climate outlooks
• Hydroclimatic monitoring using indices such as the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Aridity Anomaly Index (AAI)
• Weekly health bulletins integrating weather and climate information for climate-sensitive diseases
• Climate information services supporting disaster risk reduction and sectoral planning

These products contribute to informed decision-making by governments, disaster management authorities, and public health agencies (Figure 4).

Figure 4. Representative operational weather and climate products from the India Meteorological Department (IMD) demonstrating the integration of hydroclimatic monitoring, temperature surveillance, and heatwave climatology into climate services. These products provide timely, science-based information that supports impact-based forecasting, early warning systems, preparedness planning, and climate-informed decision-making across health, disaster management, agriculture, and water-resource sectors.

Artificial Intelligence: A New Frontier in Climate–Health Services

The increasing availability of meteorological observations, satellite data, environmental information, and health surveillance datasets has opened new opportunities for applying Artificial Intelligence (AI) to climate-health research (Figure 5).

AI and machine learning techniques can analyze complex, nonlinear relationships that are often difficult to capture using conventional statistical methods.

Potential applications include:

• Heat-health risk prediction
• Dengue and malaria outbreak forecasting
• Hospital admission forecasting during extreme weather
• Air quality health-risk prediction
• Identification of disease hotspots
• Integrated climate-health early warning systems

Importantly, AI should be viewed as a complement to physical understanding rather than a replacement. Weather and climate models provide scientifically robust forecasts of environmental conditions, while AI helps translate these forecasts into actionable health intelligence.

Figure 5. Conceptual framework illustrating the application of Artificial Intelligence in climate–health prediction. By integrating weather observations, climate forecasts, environmental information, and health surveillance data, AI and machine learning models can predict climate-sensitive health risks such as heat stress, dengue, malaria, water-borne diseases, air quality impacts, and food insecurity. The resulting risk assessments support early warning systems, preparedness, resource allocation, and climate-informed public health decision-making.

From Climate Information to Climate Services

The ultimate objective is not simply to generate weather forecasts but to convert scientific information into climate services that support public health decisions.

An effective climate-health service integrates:

• Weather observations
• Climate predictions
• Environmental monitoring
• Disease surveillance
• Artificial Intelligence
• User engagement and decision support

The outcome is actionable information that enables early warning, preparedness, and timely interventions (Figure 6).

Figure 6. Emerging applications of Artificial Intelligence in climate–health services. AI complements weather and climate prediction by integrating meteorological, climate, environmental, and health information to generate location-specific health risk assessments. These applications support heat-health early warning systems, forecasting of vector-borne and water-borne diseases, air quality health advisories, preparedness planning, and climate-informed decision-making for resilient public health systems.

Looking Ahead

The future of climate-health services lies in closer collaboration between meteorologists, epidemiologists, data scientists, public health professionals, and policymakers.

Several priorities will shape the next generation of climate-health services:

• Strengthening integration of meteorological and health datasets.
• Developing impact-based health forecasting systems.
• Leveraging Artificial Intelligence and predictive analytics.
• Improving district-level climate-health risk assessments.
• Enhancing interdisciplinary collaboration and capacity building.
• Delivering user-oriented climate services that support evidence-based public health decisions.

As climate variability and climate change continue to influence health outcomes, weather and climate information will become an increasingly valuable public health resource.

The evolution from forecasting weather to forecasting health risks represents one of the most promising frontiers of climate services. By combining advances in Earth system modeling, health surveillance, and Artificial Intelligence, we can build climate-informed health systems that are more resilient, proactive, and better prepared to protect lives.