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The effects of global warming and geoengineering on annual precipitation and its seasonality over different parts of the world are examined using the piControl, 4xCO 2 and G1 simulations from eight global climate models participating in the Geoengineering Model Intercomparison Project. Specifically, we have used relative entropy, seasonality index, duration of the peak rainy season and timing of the peak rainy season to investigate changes in precipitation characteristics under 4xCO 2 and G1 scenarios with reference to the piControl. In a 4xCO 2 world, precipitation is projected to increase over many parts of the globe, along with an increase in both the relative entropy and seasonality index. Further, in a 4xCO 2 world the increase in peak precipitation duration is found to be highest over the subpolar climatic region. However, over the tropical rain belt, the duration of the peak precipitation period is projected to decrease. Furthermore, there is a significant shift in the timing of the peak precipitation period by 15 days-2 months (forward) over many parts of the Northern Hemisphere except for over a few regions, such as North America and parts of Mediterranean countries, where a shift in the precipitation peak by 1-3 months (backward) is observed. However, solar geoengineering is found to significantly compensate many of the changes projected in a 4xCO 2 scenario. Solar geoengineering nullifies the precipitation increase to a large extent. Relative entropy and the seasonality index are almost restored back to that in the control simulations, although with small positive and negative deviations over different parts of the globe, thus, significantly nullifying the impact of 4xCO 2 . However, over some regions, such as northern parts of South America, the Arabian Sea and Southern Africa, geoengineering does not significantly nullify changes in the seasonality index seen in 4xCO 2 . Finally, solar geoengineering significantly compensates the changes in timing of the peak and duration of the peak precipitation seen in 4xCO 2 .

Using a dynamical model (VECTRI) for malaria transmission that accounts for the influence of population and climatic conditions, malaria transmission dynamics is investigated for a highly endemic region (state of Odisha) in India. The model is first calibrated over the region, and subsequently numerical simulations are carried out for the period 2000–2013. Using both model and observations we find that temperature, adult mosquito population, and infective biting rates have increased over this period, and the malaria vector abundance is higher during the summer monsoon season. Regionally, the intensity of malaria transmission is found to be higher in the north, central and southern districts of Odisha where the mosquito populations and the number of infective bites are more and mainly in the forest or mountainous ecotypes. We also find that the peak of the malaria transmission occurs when the monthly mean temperature is in the range of ~28–29 °C, and monthly rainfall accumulation in the range of ~200–360 mm.