DOI: 10.3886/e233262v2
title: Climate.gov Data Snapshots: Projections - Average Mean Temperature, High Emissions

description: <b>Q: What
 average temperatures are projected for future decades if global 
emissions of heat-trapping gases continue increasing through 2100?
</b><b>
 </b><br><b>A:</b> Colors
 show projected average daily temperature for each month from the 2020s 
through the 2090s, based on a high-emissions future.

In this case, the high-emissions future represents a specific Representative Concentration Pathway (RCP) called RCP 8.5. Learn more about RCPs » « Go back to the Data Snapshots interface

 
 <br><b>Q: Where do these measurements come from?
</b><b>
 </b><br><b>A:</b> Temperature
 projections in these images represent output from 32 global climate 
models that are all part of the Coupled Model Intercomparison Project 
Phase 5 (CMIP5).
 Projections labeled as “High emissions” represent a potential future in
 which global emissions continue increasing through the 21st 
century. By 2100, the result of this pathway is climate forcing of 8.5 
Watts per square meter at the top of the atmosphere. Based on the energy
 imbalance along this pathway, global climate models calculate 
temperature across Earth’s surface for future periods. The RCP 8.5 
scenario represents a future in which no climate policies are enacted.

To produce regionally relevant projections, results from the global 
models were statistically downscaled using a method called Localized 
Constructed Analogs (LOCA).
 This technique uses observed local-scale weather and climate 
information to increase the spatial resolution of global-scale 
projections, and corrects for bias in the model simulations.

Images of long-term averages from 1981 to 2010 (PRISM normals)
 show recent conditions; these maps provide a baseline for comparison 
with future projections. To produce the normals data, the PRISM group at
 Oregon State University gathered temperature and precipitation records 
from a range of federal, state, and international weather station 
networks, and then mapped them to a grid. To fill map areas between 
observation stations, the group used a digital elevation model as a 
predictor grid, and refined the model to account for local effects of 
mountains, distance from coasts, and other factors that affect climate 
in complex terrains.

 
 <br><b>Q: What do the colors mean?</b><b>
 </b><br><b>A:</b> Shades
 of blue show where average temperature for the month was, or is 
projected to be, below 60°F during the period indicated. The darker the 
shade of blue, the lower the temperature. Areas shown in shades of 
orange and red had, or are projected to have, average temperatures over 
60°F. The darker the shade of orange or red, the higher the temperature.
 White or very light colors show where the average temperature was, or 
is projected to be, near 60°F.

 
 <br><b>Q: Why do these data matter?</b><b>
 </b><br><b>A:</b> In
 order to meet future needs for energy, food, and public health, 
planners and other decision makers need to understand how temperatures 
are projected to change over the coming decades. As the climate system 
continues responding to the heat-trapping gases we have added to the 
atmosphere, temperatures will change at different rates in different 
regions. These images can help people get a sense of how much warming 
their region will experience each decade so they can plan ahead for new 
conditions.

These data also provide people with a way to compare conditions 
projected for stabilized emissions with conditions projected for high 
emissions. Comparing the two potential futures may encourage people to 
take actions to reduce emissions. 

 
 <br><b>Q: How did you produce these snapshots?</b><b>
 </b><br><b>A:</b> We
 used a suite of Python scripts to process and visualize LOCA (Localized
 Constructed Analogs) data. The processing scripts averaged the daily 
values for each month in a given decade from all 32 global climate 
models that comprise the LOCA dataset. We then calculated the median of 
all models in each month of the decade. The visualization scripts 
produced maps of the results within the contiguous United States.

For further information, see the README file or access the scripts on GitHub ».

 
 
 <br><b>Additional information</b>
 Observed 
temperatures for the last three decades (1981-2010) fall mostly within 
the range from 0-100°F; this is the temperature range we used to produce
 sets of 30-yr average temperature maps for Data Snapshots. The range 
was selected to reveal patterns of recent temperature across the maps.

In order to generate a consistent set of maps that represent recent 
AND future temperatures, we needed to show temperatures from 10-110°F. 
Therefore, if you compare Data Snapshots’ PRISM normals images with our 
30-year average images side-by-side, you will see slightly different 
colors representing the same data values. Always interpret color-coded 
images by checking the numerical values shown on their color bars.

 
 <br><b>References</b>
 
 Pierce, D. W., D. R. Cayan, and B. L. Thrasher, 2014: Statistical Downscaling Using Localized Constructed Analogs (LOCA). Journal of Hydrometeorology, volume 15, page 2558-2585. <br>PRISM 30-year Normals Descriptions of PRISM Spatial Climate Datasets for the Conterminous United States
 <br><br>Source: https://www.climate.gov/maps-data/data-snapshots/data-source/projections-average-mean-temperature-hi...<br><br>This upload includes two additional files:<br>* Projections - Average Mean Temperature, High Emissions _NOAA Climate.gov.pdf is a screenshot of the main Climate.gov site for these snapshots (https://www.climate.gov/maps-data/data-snapshots/data-source/projections-average-mean-temperature-hi...)<br>* Cimate_gov_ Data Snapshots.pdf is a screenshot of the data download page for the full-resolution files.<br><br>
description: Data is divided in to folders by image resolution -- full size (zip), broadcast (png), small (png), and large (png). Occasionally there may be one image missing in a particular size but available in another size.<br><br>Data downloaded from climate.gov 6/15/2025-6/16/2025 (small, large, and full resolution) and 6/21/2025 (broadcast).<br>