National temperature outlook

Warmer season more likely over northern and eastern Australia

Text details of chance of warmer maximum and minimum temperatures


  • Warmer days are more likely over most of the northern tropics, the eastern States, southeast SA, and the south coast of WA, with greatest odds for southern Victoria and Tasmania
  • The chances of warmer or cooler days are roughly equal over the north of Cape York Peninsula, most of southern and central WA, the southwest of the NT, and western and central SA
  • Warmer nights are more likely over most of Australia, except the northeast of the NT, parts of the Pilbara in WA, and the Cape York Peninsula in Queensland. The highest odds are over southeast Australia
  • Climate influences include a warming tropical Pacific, and a warm to near-normal Indian Ocean
  • Outlook accuracy for maximum temperatures is high over the northern half of Australia, and moderate elsewhere, except for southeast WA where the accuracy is low
  • Minimum temperature accuracy is moderate to high over most of Australia, except for southeast WA, the far southeast of SA, and the far northeast of NSW where accuracy is low to very low.
Probability of exceeding median maximum temperature, large image Probability of exceeding median minimum temperature, large image


The April to June maximum temperature outlook shows chances of greater than 60% for warmer than normal days over most of the tropical north, the eastern States, southeast SA, and south coastal WA. Chances exceed 80% over southern Victoria and Tasmania. So for every ten April to June outlooks with similar odds to these, about six to eight of them would be warmer than average over these areas, while about two to four would be cooler.

Over the Cape York Peninsula in Queensland, southern and central WA, the southwest of the NT, and most of SA the chances of warmer or cooler daytime temperatures are roughly equal.

The chances that the average minimum temperature for April to June will exceed the long-term median are greater than 60% over most of the country. Chances exceed 80% over southeastern Australia.

The chances of receiving cooler or warmer than normal night-time temperatures for April to June is roughly equal (i.e., close to 50%) over the parts of the Pilbara in WA, the northeast of the NT, and Cape York Peninsula in Queensland.

Climate influences

The El Niño-Southern Oscillation (ENSO) currently remains neutral, but the tropical Pacific is currently warming. Dynamical models surveyed by the Bureau indicate that further warming of the tropical Pacific is likely in the coming months, with most models approaching or exceeding El Niño thresholds during the southern winter.

Sea surface temperatures surrounding Australia, and to the west, are expected to be near normal to warmer than normal.

The Indian Ocean Dipole (IOD) influence is minimal during the first part of the outlook period, with a neutral IOD likely for the second part.

How accurate is the outlook?

Maximum temperature outlook accuracy for the April to June period is:

  • High over the northern half of Australia
  • Moderate elsewhere, except for
  • southeast WA where accuracy is low

Minimum temperature outlook accuracy for the April to June period is:

  • Moderate to high over most of Australia, except for
  • southeast WA, the far southeast of SA, and the far northeast of NSW where accuracy is low to very low

Further information

(03) 9669 4057

Model accuracy

Model accuracy (also known as model confidence or model skill) is a measure of how well a model has performed at that time of year in the past. One way that the Bureau measures the accuracy of its climate models is by comparing how often the outlook favoured a particular category (for example, when above median rainfall was more likely to occur than below median rainfall), and that favoured category was then subsequently observed. This measurement of accuracy is known as "Percent Consistent", and has been tested for the official seasonal outlook model over the period from 1981 to 2010.

The accuracy levels on the maps give an indication of how well the outlooks match the observed outcomes. High accuracy means that tests of the model on historical data show a strong relationship between the most likely outlook category (above or below median) and the subsequent observation (above or below median). In areas with high accuracy, historically the model has performed very well, and hence a high degree of confidence can be placed in future outlooks. On the other hand, low accuracy means the model has not performed well in these regions and therefore the outlook should be used with caution. In the places and seasons where the outlooks are most skilful, the category of the eventual outcome (above or below median) is consistent with the category favoured in the outlook about 75% of the time. In the least skilful areas, the outlooks perform no better than random chance (equivalent to the "flip of a coin").

A random forecast of above median rainfall will be correct about 50% of the time. For this reason, the green shading on the map shows areas where the model has greater than 50% accuracy only. In areas which are not coloured in green on the map, some caution should be taken when using the forecast, notably at times when there is not a strong climate influence (for example, no El Niño or La Niña is present).

The maximum temperature outlook accuracy is good for most of the year, with the lowest point during the winter seasons. Of the variables predicted (i.e. rainfall, and maximum and minimum temperature), maximum temperature performs best. The minimum temperature outlook accuracy peaks during summer, late autumn and late spring. Accuracy is lowest during late summer and late winter.

As a guide, the Bureau uses the following terminology when referring to the accuracy of the outlooks:

Map keyTerminology
Legend of the model accuracy map 75% and above - very high
65 to 75% - high
55 to 65% moderate
50 to 55% - low
45 to 50% - low
Below 45% - very low

What is normal for this period?

These maps show the median (or 50th percentile) maximum and minimum temperature for the given three months. The median temperatures are calculated from the 1981-2010 period.

The maps will differ from other median maps on the Bureau's website. This is because the dynamical model forecasts use an averaging period of 1981-2010. The quality of the dynamical model forecasts is in-part determined by the coverage and accuracy of the observations fed into it. Therefore, to be consistent from one year to the next, the Bureau has only run the model during the modern satellite era.

Median rainfall for January to March

About the outlook

Using the outlook

  • The Bureau's rainfall seasonal climate outlooks are general statements about the likelihood of wetter or drier than average weather over a three-month period. The probabilities are generated from the Predictive Ocean Atmosphere Model for Australia (POAMA), the Bureau of Meteorology's dynamical climate model. It is important to note that they are not categorical predictions about future rainfall, and hence the success or failure of one individual outlook does not infer that the model has low skill. Skill is assessed over multiple runs of the model. Likewise, temperature outlooks give the likelihood or chance of exceeding the average maximum and minimum temperatures over the entire three-month outlook period. Information about whether individual weeks or months may be unusually hot or cold, is presently unavailable.

  • Probability outlooks should not be used as if they were categorical (yes/no) forecasts. These outlooks should be used as a tool in risk management and decision making. Greatest benefits accrue from long-term use, say over 10 years.

About the model

  • The seasonal climate outlooks are generated by the Predictive Climate Ocean Atmosphere Model for Australia (POAMA), a dynamical (physics based) climate model developed by the Bureau of Meteorology and CSIRO Marine and Atmospheric Research. This coupled atmosphere-ocean model is a state of the art seasonal forecast system. Read more about POAMA.

  • The POAMA model is undergoing continuous research and development. Advances in the science of seasonal prediction, improvements in the observations and how they are fed into the model, as well as increases in supercomputing power are just some of the ways the model's accuracy will increase over time.

El Niño and La Niña

Indian Ocean Dipole

Statistical model outlooks

The official dynamical outlooks supercede the statistical outlooks. Statistical outlook maps will continue to be available for a review period: