Background

Climate change may expand dengue transmission in space and season across Central America. In Costa Rica, complex topography and very small districts mean coarse global climate models can miss local conditions that drive outbreaks, creating a need for district-level, high-resolution climate–dengue assessments. This study aims to: (1) model the climate–dengue relationship at the district level using high-resolution data; (2) identify the best climate predictors for dengue incidence; and (3) provide mid-century (2035–2065) dengue cases projections under a pessimistic scenario (SSP5-8.5) with seasonal windows actionable by region.

Methods

Precipitation and temperature indices derived from the Climate Hazards group Infrared Precipitation with Stations (CHIRPs) and Climate Hazards Center Infrared Temperature with Stations (CHIRTs) were related to dengue diagnoses from Costa Rica’s public health centers using a linear model. An objective algorithm selected parsimonious climate–dengue predictors, with cross-validation to prevent overfitting. The resulting quasi-optimal models combined with downscaled projections from an ensemble of eight General Circulation Models (GCMs) to estimate future dengue incidence changes at the district level, Costa Rica’s smallest administrative division.

Results

Temperature and precipitation data are significantly related to dengue counts. Temperature dominates most district models during the dry season (December to June), while precipitation dominates during the rainy season (July–October). Mid-century projections indicate increases of up to 42 additional cases in some districts compared to the historical baseline, with the location of the most pronounced changes varying by month.

Conclusions

The projected dengue increases presented here are driven solely by climate change under the most pessimistic greenhouse gas (GHG) concentration scenario, and thus represent a potential upper bound on future risk. These findings offer actionable guidance on where and when dengue incidence may rise, and should inform adaptive health policies aimed at reducing the impacts of climate change in high-risk areas.

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