What you can see in this figure
This map illustrates projected changes in various climate variables under different Shared Socioeconomic Pathways (SSPs), which are future climate scenarios. You can visualize these changes as either annual or seasonal shifts.

The data can be presented in two ways:

• Ensemble Anomaly: This shows the projected change relative to a historical period.
• Absolute Projected Climate Variable (mean): This displays the actual projected value of the climate variable, covering both the historical period and extending into the future.

The p10 and p90 values indicate the 10th and 90th percentile ranges among the different climate models in the multi-model ensemble, providing insight into the uncertainty or spread of projections.

Understanding the Data: Implications and Utility
These findings allow us to identify which seasons are experiencing the most significant changes, as well as regions undergoing more rapid or slower climatic shifts. The p10 and p90 values are crucial here, as they provide an indication of the agreement among different models regarding a particular change in a specific region. A narrower range between p10 and p90 suggests higher model consensus.

Understanding how temperature and precipitation vary in each region is essential for planning in sectors such as agriculture, water resource management, and flood risk mitigation. For instance, high temperatures combined with low rainfall can exacerbate drought, while intense rainfall following dry periods can increase flood risk due to reduced soil absorption.

Temperature changes will depend on topography, land cover, proximity to water bodies, and atmospheric circulation patterns. You'll often observe greater projected absolute warming in higher latitudes, particularly prominent in the Northern Hemisphere.

• Maximum temperatures (day temperatures) are particularly important for assessing heat stress, wildfire risk, and drought conditions. Heat stress is particularly relevant in urban areas where the heat island effect can intensify impacts.
• Minimum temperatures (night temperatures) are critical for human health (e.g., sleep quality), animal health, agricultural productivity (e.g., frost risk), and ecosystem stability.

We provide a set of complementary climate indicators that go beyond basic temperature and precipitation data. These include metrics such as the number of days exceeding critical heat and humidity thresholds, which are particularly relevant for sectors like agriculture, public health, and urban planning.

Recognizing that different regions have different sensitivities, we offer multiple threshold levels to reflect local priorities and vulnerabilities. For example:

• Number of hot days (e.g., days above 35°C) can indicate heat stress risks for outdoor labor.
• Number of tropical nights (e.g., nights above 26°C) is important for sleep quality and agricultural crops.
• Number of hot and humid days (e.g., high temperature combined with high humidity) highlights conditions that pose serious risks for heat-related illnesses.

It's important to note that precipitation is considerably more variable and complex than temperature, making it much harder to model and interpret, but the theoretical expectation for precipitation is that dry regions and dry seasons will become drier, while wet regions and wet seasons will become wetter. Extreme precipitation is posed to increase in most regions. 

What you can see in this figure
This figure illustrates projected changes in various climate variables under different SSPs for each month and for a given spatial unit (to select on the plot above). You can select and deselect SSPs directly from the figure's legend. 

The data can be presented in two ways (Calculation selection in the filters above):

• Ensemble Anomaly: This shows the projected change relative to a historical period for each month. This is the most correct scientific way of understanding multi-model changes.
• Absolute Projected Climate Variable (mean): This displays the actual projected value of the climate variable, covering both the historical period and extending into the future.

The p10 and p90 shading indicate the 10th and 90th percentile ranges among the different climate models in the ensemble, providing insight into the uncertainty or spread of monthly projections.

Understanding the Data: Implications and Utility
These findings allow us to identify which months are experiencing the most significant changes when compared to the historical seasonal cycle. The p10 and p90 values are crucial here, as they provide an indication of the agreement among different models regarding a particular change in a specific region. A narrower range between p10 and p90 suggests higher model consensus. While temperature variables are generally projected to trend towards greater warming, precipitation changes are often more complex and less straightforward to predict.

What you can see in this figure
This candlestick graph displays, for each 20-year period and scenario, the spread of CMIP6 model projections for a given variable. It highlights the median, the 10th and 90th percentiles, as well as the individual model results. You can click interactively to explore details for each model.

Understanding the Data: Implications and Utility
This dispersion reflects the uncertainty and range of projections within the CMIP6 ensemble. While early-century projections cluster more tightly, dispersion generally increases later in the century as internal variability accumulates and models diverge in their responses. By examining the spread of results, we can better understand the degree of consensus or divergence among models and make more informed decisions regarding climate projections and their potential impacts.