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• Magnitude-type clustering of dengue incidence. • Time-lag multiple regression model linking weather to clustered dengue incidence. • Comparison between division into tertiles and optimal-barriers-based clustering. • Two forecasting methods for predicting dengue incidence. Dengue incidence has been increasing dramatically in last few years with nearly four hundred million annual cases worldwide. It has been postulated that the wide-spread of dengue be due to climate change and increased exposure following the increasing human population in the affected regions. Climate change impacts on ecosystem have also set a critical role in the unpredictability of vector breeding behavior. A compelling strategy in the modeling of dengue outbreak must therefore integrate climate factors inasmuch as they determinedly govern incidence patterns. The aim of this paper is to construct a clustering integrated multiple regression model for predicting dengue incidence rate based on incidence, rainfall, and humidity data, which renders early warning information. The data used were dengue incidence data in Jakarta obtained from Jakarta Health Office and meteorological data from Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG) in the period 2008–2016, defined on weekly basis. Cross-correlation was used to determine the interrelationship between dengue, rainfall, and relative humidity in Jakarta. Further improvement of the model was done by instrumenting the accumulated preceding one-month dengue incidence as an additional correction term in the model. The best fittings in terms of outbreak catchment and minimal mean squared error were obtained from the model variants involving the accumulated original and logarithm of the incidence rates respectively. Both the historical incidence rate locale and centroids of the meteorological data related to the clustering as well as the accumulated incidence rate serve as the key determinant for the upcoming incidence rate. An optimal clustering was determined in a way that the mean squared error achieves its foremost minimum, which almost coincides with the division into tertiles. These clustering strategies can be utilized to provide a more accurate forecast of the ominous dengue incidence for a few weeks' lead-time. [ABSTRACT FROM AUTHOR]