  Local climate conditions play a major role in the development of the mosquito
population responsible for transmitting Dengue Fever. Since the {\em Aedes
Aegypti} mosquito is also a primary vector for the recent Zika and Chikungunya
epidemics across the Americas, a detailed monitoring of periods with favorable
climate conditions for mosquito profusion may improve the timing of
vector-control efforts and other urgent public health strategies. We apply
dimensionality reduction techniques and machine-learning algorithms to climate
time series data and analyze their connection to the occurrence of Dengue
outbreaks for seven major cities in Brazil. Specifically, we have identified
two key variables and a period during the annual cycle that are highly
predictive of epidemic outbreaks. The key variables are the frequency of
precipitation and temperature during an approximately two month window of the
winter season preceding the outbreak. Thus simple climate signatures may be
influencing Dengue outbreaks even months before their occurrence. Some of the
more challenging datasets required usage of compressive-sensing procedures to
estimate missing entries for temperature and precipitation records. Our results
indicate that each Brazilian capital considered has a unique frequency of
precipitation and temperature signature in the winter preceding a Dengue
outbreak. Such climate contributions on vector populations are key factors in
dengue dynamics which could lead to more accurate prediction models and early
warning systems. Finally, we show that critical temperature and precipitation
signatures may vary significantly from city to city, suggesting that the
interplay between climate variables and dengue outbreaks is more complex than
generally appreciated.
