**1. Introduction**

Tropical cyclones (TCs) frequently affect coastal communities of Australia and island nations in the Indian and Pacific oceans, and pose significant threat to life and property. In many cases TC impacts on island countries were devastating [1]. Knowledge about TC variability (spatial and temporal) is important for improving preparedness and resource mobilisation well in advance of potential TC impacts, to reduce risk of multi-hazards associated with TCs.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

TC activity is affected by climate variability and climate change. Large-scale climatic modes such as the El Niño-Southern Oscillation (ENSO) are known to modulate TC occurrences [2]. In addition, modelling of future climate suggests likely changes in TC activity. As stated in Chapter 14, Climate Phenomena and their Relevance for Future Climate Change of the IPCC Fifth assessment report: 'Based on process understanding and agreement in twenty-first century projections, it is *likely* that the global frequency of occurrence of tropical cyclones will either decrease or remain essentially unchanged, concurrent with a *likely* increase in both global mean tropical cyclone maximum wind speed and precipitation rates' [3].

To improve our knowledge about historical TCs in the Indian and Pacific oceans and develop accurate methodologies for TC seasonal forecasting, 'Climate Change and Southern Hemisphere Tropical Cyclones' International Initiative has been established in 1999 [4]. This International Initiative addressed three key areas: (i) preparing high quality TC historical database, (ii) producing TC climatology and (iii) developing skilful methodologies for TC seasonal prediction.

TC seasonal forecasting is one of the important elements of a Climate Risk Early Warning System (CREWS) aiming to increase preparedness of coastal communities atrisk. Overthe last few decades, statistical model-based methods for TC seasonal forecasting have been developed, starting with the pioneering work of Gray [5]. Statistical models are based on historical relationships of TC activity with large-scale environmental drivers which modulate TC activity such as the ENSO. Relating the observed numbers of TCs with ENSO indices it is possible to derive linear regression equations which can be used for prediction of future cyclone activity. The TC-ENSO relationship was used in developing statistical methodology for forecasting seasonal TC activity in the Australian and some other regions [6–8].

However, in a globally warming environment, statistical models may not produce reliable outcomes when values of ENSO indices are outside of the range of historical records. While the developed statistical models performed reasonably well in the past, during a very strong La Niña event in 2010–2011 the statistical models significantly over-predicted the number of TCs in the Australian region [9]. It became evident that improving statistical methodologies and developing new dynamical climate model-based methodologies is essential to improve prediction skill.

In this chapter, prospects for improving the skill of operational seasonal prediction of TC activity in the regions of the Southern Hemisphere (SH) using statistical and dynamical modelbased approaches are presented.
