Argentina

This page presents Argentina's climate context for the current climatology, 1991-2020, derived from observed, historical data. Information should be used to build a strong understanding of current climate conditions in order to appreciate future climate scenarios and projected change. You can visualize data for the current climatology through spatial variation, the seasonal cycle, or as a time series. Analysis is available for both annual and seasonal data. Data presentation defaults to national-scale aggregation, however sub-national data aggregations can be accessed by clicking within a country, on a sub-national unit.  Other historical climatologies can be selected from the Time Period dropdown list. 

Observed, historical data is produced by the Climatic Research Unit (CRU) of University of East Anglia. Data is presented at a 0.5º x 0.5º (50km x 50km) resolution. This dataset is based on ground-based weather station observations dating back to 1900. However, in regions with sparse station coverage—particularly during the early 20th century—data quality may be affected by spatial and temporal gaps, leading to potential biases. All data is accessible in CCKP's Data Download.

 

What you can see in this figure
This map displays historical patterns of temperature (average, maximum, minimum) and precipitation across various land regions. Users can interact with the tool by selecting different variables, time periods, seasons, as well as choosing specific countries or subnational administrative units. These selections dynamically update the visual plots below.

Understanding the Data: Implications and Utility
Understanding the average ranges that characterize a selected climatology 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 patterns are primarily influenced by elevation and latitude—higher elevations and latitudes generally experience cooler temperatures. However, regional temperature variations are also shaped by other factors such as land cover, proximity to water bodies, urbanization, and atmospheric circulation patterns.

  • 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.

Precipitation patterns are broadly organized by climatic zones and rain fronts, but at finer scales, they are influenced by topography, distance from the coast, prevailing winds, and local convection processes. 

This tool provides a foundation for exploring these dynamics, helping users identify key climate patterns and potential vulnerabilities across different regions and seasons.

What are some caveats and potential limitations to consider?
This dataset is based on ground-based weather station observations dating back to 1900. However, in regions with sparse station coverage—particularly during the early 20th century—data quality may be affected by spatial and temporal gaps, leading to potential biases. Only basic temperature and precipitation variables are available. For a more in depth analysis, we recommend using instead ERA5 historical data. 

What you can see in this figure
Temperature, precipitation, and other climate dynamics change throughout a 12 month period. Understanding these patterns can help to identify local climate behavior and the potential for changing seasons over time. This chart presents essential climate variables to provide a snapshot into the seasonality of selected area. This chart is dynamic and elements can be turned on/off by selecting variables in legend. 

Understanding the Data: Implications and Utility
Users should use this chart to understand:

  • When is the wet season? Are there multiple rainy seasons?
  • How intense is the dry season and does it overlap with times of the year with highest temperatures? What might this mean for risk and need for adaptation/ resilience efforts?
  • Are there significant temperature swings - throughout the year and/or between minimum and maximum temperatures?

What you can see in this figure
This graph presents the annually averaged time series from 1901 to the present, offering insights into long-term climate behavior. 

Understanding the Data: Implications and Utility
Users are encouraged to examine two key aspects:

  1. Interannual variability– How much do values fluctuate from one year to the next? This helps define what constitutes a "normal" range of year-to-year variation.
  2. Long-term trends – Are there clear patterns of warming, drying, or other shifts over time?

Year-to-year fluctuations are influenced by many factors, but one of the most prominent global drivers is the El Niño–Southern Oscillation (ENSO), which can significantly affect both temperature and precipitation patterns. Understanding variability means not just looking at changes from one year to the next, but also recognizing if decadal fluctuations are present. These reflect natural cycles and should be considered when looking at short-term trends.

Temperature trends show a clear warming signal in most regions, particularly from the late 20th century onward.
Precipitation trends, however, are more complex and regionally variable, often influenced by local geography, atmospheric circulation, and oceanic patterns.

What you can see in this figure
A Warming Stripes graph provides a visual representation of the change in selected temperature or precipitation variable. Each bar represents the the annual average for a year. Relatively warmer is represented as yellow to red colors, while relative cooling is represented as blue. For rain, bluer means wetter. Hover over the graph to view the exact annual values for each year, allowing for more detailed exploration of the data.

Understanding the Data: Implications and Utility
This visualization makes it easy to spot both gradual trends and abrupt shifts in climate.