The average temperature in December 2024 was 38.25 degrees Fahrenheit in the United States, the fourth-largest country in the world. The country has extremely diverse climates across its expansive landmass. Temperatures in the United States On the continental U.S., the southern regions face warm to extremely hot temperatures all year round, the Pacific Northwest tends to deal with rainy weather, the Mid-Atlantic sees all four seasons, and New England experiences the coldest winters in the country. The North American country has experienced an increase in the daily minimum temperatures since 1970. Consequently, the average annual temperature in the United States has seen a spike in recent years. Climate Change The entire world has seen changes in its average temperature as a result of climate change. Climate change occurs due to increased levels of greenhouse gases which act to trap heat in the atmosphere, preventing it from leaving the Earth. Greenhouse gases are emitted from various sectors but most prominently from burning fossil fuels. Climate change has significantly affected the average temperature across countries worldwide. In the United States, an increasing number of people have stated that they have personally experienced the effects of climate change. Not only are there environmental consequences due to climate change, but also economic ones. In 2022, for instance, extreme temperatures in the United States caused over 5.5 million U.S. dollars in economic damage. These economic ramifications occur for several reasons, which include higher temperatures, changes in regional precipitation, and rising sea levels.

The average temperature in the contiguous United States reached 55.5 degrees Fahrenheit (13 degrees Celsius) in 2024, approximately 3.5 degrees Fahrenheit higher than the 20th-century average. These levels represented a record since measurements started in 1895. Monthly average temperatures in the U.S. were also indicative of this trend. Temperatures and emissions are on the rise The rise in temperatures since 1975 is similar to the increase in carbon dioxide emissions in the U.S. Although CO₂ emissions in recent years were lower than when they peaked in 2007, they were still generally higher than levels recorded before 1990. Carbon dioxide is a greenhouse gas and is the main driver of climate change. Extreme weather Scientists worldwide have found links between the rise in temperatures and changing weather patterns. Extreme weather in the U.S. has resulted in natural disasters such as hurricanes and extreme heat waves becoming more likely. Economic damage caused by extreme temperatures in the U.S. has amounted to hundreds of billions of U.S. dollars over the past few decades.

Based on current monthly figures, on average, German climate has gotten a bit warmer. The average temperature for January 2025 was recorded at around 2 degrees Celsius, compared to 1.5 degrees a year before. In the broader context of climate change, average monthly temperatures are indicative of where the national climate is headed and whether attempts to control global warming are successful. Summer and winter Average summer temperature in Germany fluctuated in recent years, generally between 18 to 19 degrees Celsius. The season remains generally warm, and while there may not be as many hot and sunny days as in other parts of Europe, heat waves have occurred. In fact, 2023 saw 11.5 days with a temperature of at least 30 degrees, though this was a decrease compared to the year before. Meanwhile, average winter temperatures also fluctuated, but were higher in recent years, rising over four degrees on average in 2024. Figures remained in the above zero range since 2011. Numbers therefore suggest that German winters are becoming warmer, even if individual regions experiencing colder sub-zero snaps or even more snowfall may disagree. Rain, rain, go away Average monthly precipitation varied depending on the season, though sometimes figures from different times of the year were comparable. In 2024, the average monthly precipitation was highest in May and September, although rainfalls might increase in October and November with the beginning of the cold season. In the past, torrential rains have led to catastrophic flooding in Germany, with one of the most devastating being the flood of July 2021. Germany is not immune to the weather changing between two extremes, e.g. very warm spring months mostly without rain, when rain might be wished for, and then increased precipitation in other months where dry weather might be better, for example during planting and harvest seasons. Climate change remains on the agenda in all its far-reaching ways.

The highest average temperature recorded in 2024 until November was in August, at 16.8 degrees Celsius. Since 2015, the highest average daily temperature in the UK was registered in July 2018, at 18.7 degrees Celsius. The summer of 2018 was the joint hottest since institutions began recording temperatures in 1910. One noticeable anomaly during this period was in December 2015, when the average daily temperature reached 9.5 degrees Celsius. This month also experienced the highest monthly rainfall in the UK since before 2014, with England, Wales, and Scotland suffering widespread flooding. Daily hours of sunshine Unsurprisingly, the heat wave that spread across the British Isles in 2018 was the result of particularly sunny weather. July 2018 saw an average of 8.7 daily sun hours in the United Kingdom. This was more hours of sun than was recorded in July 2024, which only saw 5.8 hours of sun. Temperatures are on the rise Since the 1960s, there has been an increase in regional temperatures across the UK. Between 1961 and 1990, temperatures in England averaged nine degrees Celsius, and from 2013 to 2022, average temperatures in the country had increased to 10.3 degrees Celsius. Due to its relatively southern location, England continues to rank as the warmest country in the UK.

• Parenthesis values are Celsius.

The dataset provides a comprehensive overview of the weather conditions across all cities of the world for a period of 12 months. It contains information on the average temperature in Celsius and Fahrenheit. This dataset is a valuable resource for researchers, meteorologists, and climate scientists who seek to understand the impact of climate change on different parts of the world. The data can be used to analyze trends in temperature, to develop predictive models for weather forecasting, and to evaluate the effectiveness of climate policies. The information in this dataset is updated regularly, ensuring that users have access to the most recent and accurate weather data available. With this dataset, users can gain valuable insights into the complex relationship between climate and the environment, and make informed decisions about climate change mitigation and adaptation strategies.

Description: ChatGPT

Contained within the 3rd Edition (1957) of the Atlas of Canada is a plate that shows four maps of the mean daily temperatures for January, April, July and October averaged over the 30 year period, circa 1921-1950. The mean temperature for any day is the average of the maximum and minimum temperatures for that day. The mean daily temperature for any month is the average of the mean temperatures for each day of that month.

The Daily Air Temperature and Heat Index data available on CDC WONDER are county-level daily average air temperatures and heat index measures spanning the years 1979-2010. Temperature data are available in Fahrenheit or Celsius scales. Reported measures are the average temperature, number of observations, and range for the daily maximum and minimum air temperatures, and also percent coverage for the daily maximum heat index. Data are available by place (combined 48 contiguous states, region, division, state, county), time (year, month, day) and specified maximum and minimum air temperature, and heat index value. The data are derived from the North America Land Data Assimilation System (NLDAS) through NLDAS Phase 2, a collaboration project among several groups: the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) Environmental Modeling Center (EMC), the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC), Princeton University, the National Weather Service (NWS) Office of Hydrological Development (OHD), the University of Washington, and the NCEP Climate Prediction Center (CPC). In a study funded by the NASA Applied Sciences Program/Public Health Program, scientists at NASA Marshall Space Flight Center/ Universities Space Research Association developed the analysis to produce the data available on CDC WONDER.

This map shows trends in unusually hot and cold temperatures at individual weather stations that have operated consistently since 1948. In this case, the term “unusually hot” refers to a daily maximum temperature that is hotter than the 95th percentile temperature during the 1948–2023 period. Thus, the maximum temperature on a particular day at a particular station would be considered “unusually hot” if it falls within the warmest 5 percent of measurements at that station during the 1948–2023 period. The map shows changes in the total number of days per year that were hotter than the 95th percentile. Red upward-pointing symbols show where these unusually hot days are becoming more common. Blue downward-pointing symbols show where unusually hot days are becoming less common. For more information: www.epa.gov/climate-indicators.

The monthly average temperature in the United States between 2020 and 2025 shows distinct seasonal variation, following similar patterns. For instance, in April 2025, the average temperature across the North American country stood at 12.02 degrees Celsius. Rising temperatures Globally, 2016, 2019, 2021 and 2024 were some of the warmest years ever recorded since 1880. Overall, there has been a dramatic increase in the annual temperature since 1895. Within the U.S. annual temperatures show a great deal of variation depending on region. For instance, Florida tends to record the highest maximum temperatures across the North American country, while Wyoming recorded the lowest minimum average temperature in recent years. Carbon dioxide emissions Carbon dioxide is a known driver of climate change, which impacts average temperatures. Global historical carbon dioxide emissions from fossil fuels have been on the rise since the industrial revolution. In recent years, carbon dioxide emissions from fossil fuel combustion and industrial processes reached over 37 billion metric tons. Among all countries globally, China was the largest emitter of carbon dioxide in 2023.

Water is an essential ingredient to life on Earth. In its three phases (solid, liquid, and gas), water continuously cycles within the Earth and atmosphere to create significant parts of our planet’s climate system, such as clouds, rivers, vegetation, oceans, and glaciers. Precipitation is a part of the water cycle, where water particles fall from clouds in the form of rain, sleet, snow, ice crystals, or hail. So how does precipitation form? As water on Earth’s surface evaporates it changes from liquid to gas and rises into the atmosphere. Because air cools as altitude increases, the vapor rises to a point in the atmosphere where it cools enough to condense into liquid water or freeze into ice, which forms a cloud. Water vapor continues to condense and stick to other water droplets in the cloud until the weight of the accumulated water becomes too heavy for the cloud to hold. If the air in the cloud is above freezing (0 degrees Celsius or 32 degrees Fahrenheit), the water falls to the Earth as rain. If the air in the cloud is below freezing, ice crystals form and it snows if the air between the cloud and the ground stays below 0 degrees Celsius (32 degrees Fahrenheit). If a snowflake falls through a warmer part of a cloud, it can get coated in water, then refrozen multiple times as it circulates around the cloud. This forms heavy pellets of ice, called hail, that can fall from the sky at speeds estimated between 14 and 116 kmph (9 and 72 mph) depending on its size. A hailstone can range from the size of a pea (approximately 0.6 cm or 0.25 inches) to a golf ball (approximately 4.5 cm or 1.75 inches), and sometimes even reach the size of a softball (approximately 10 cm or 4 inches).Precipitation doesn’t fall in the same amounts throughout the world. The presence of mountains, global winds, and the unequal distribution of land and sea cause some parts of the world to receive greater amounts of precipitation compared with others. Areas with rising moist air generally indicate regions with high precipitation. According to the Köppen Climate Classification System, tropical wet and tropical monsoon climates receive annual precipitation of 150 cm (59 inches) or greater. Tropical wet regions, where rain occurs year-round, are found near the equator in central Africa, the Amazon rainforest, and southern India. Monsoons are storms with large patterns of wind and heavy rain that can span over a continent. Tropical monsoon climates are located mainly in Southeast Asia and areas around the Pacific Ocean, where annual rainfall is equal to or greater than areas with a tropical wet climate. Here, intense monsoon rains fall during the three hottest months of the year, which are usually between June and October. Snow and ice, which are most common in high altitudes and latitudes, cover most of the Earth’s polar regions. High altitude regions of the Andes, Tibetan Plateau, and the Rocky Mountains maintain some amount of snow cover year-round.Over the next century, it is predicted warming global temperatures will increase the temperature of the ocean and increase the speed of the water cycle. With a quicker rate of evaporation, there will be more water in the atmosphere, allowing clouds to produce heavier precipitation and more intense storms. Although storms would be more intense in wetter regions, increased evaporation could also lead to extreme drought in drier areas of the world. This would greatly affect farmers who grow crops in dry locations like Southern California or Kansas.This map layer shows Earth's mean precipitation (measured in centimeters per month) averaged from 1981 to 2012 as calculated but the Copernicus Climate Change Service. The data was collected from the Copernicus satellite and validated with precipitation measurements from weather stations. Scientists averaged all of the amounts (originally collected in meters) occurring each month together, and they calculated the average of each month over 30 years to create this map.

This dataset contains monthly temperature records for all states in Mexico from January 1985 to February 2025. The data includes temperatures in both Celsius and Fahrenheit, with three key metrics:Minimum average temperature for the monthMaximum average temperature for the monthOverall mean temperature for the monthAdditionally, this project includes:A visualization script that generates temperature trend charts efficientlyA sample chart illustrating temperature evolution in Mexico CityA requirements.txt file specifying dependencies for the scriptThe temperature data was sourced from the Mexican National Meteorological Service (SMN): SMN - Monthly Temperature Summaries.This dataset is useful for climate analysis, trend studies, and data visualization projects related to temperature variations across Mexico.

Context

Who among us doesn't talk a little about the weather now and then? Will it rain tomorrow and get so cold to shake your chin or will it make that cracking sun? Does global warming exist?

With this dataset, you can apply machine learning tools to predict the average temperature of Detroit city based on historical data collected over 5 years.

Content

The given data set was produced from the Historical Hourly Weather Data [https://www.kaggle.com/selfishgene/historical-hourly-weather-data], which consists of about 5 years of hourly measurements of various weather attributes (eg. temperature, humidity, air pressure) from 30 US and Canadian cities.

From this rich database, a cutout was made by selecting only the city of Detroit (USA), highlighting only the temperature, converting it to Celsius degrees and keeping only one value for each date (corresponding to the average daytime temperature - from 9am to 5pm).

In addition, temperature values ​​were artificially and gradually increased by a few Celsius degrees over the available period. This will simulate a small global warming (or is it local?)...

In summary, the available dataset contains the average daily temperatures (collected during the day), artificially increased by a certain value, for the city of Detroit from October 2012 to November 2017.

The purpose of this dataset is to apply forecasting models in order to predict the value of the artificially warmed average daily temperature of Detroit.

See graph in the following image: black dots refer to the actual data and the blue line represents the predictive model (including a confidence area).

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Acknowledgements

This dataset wouldn't be possible without the previous work in Historical Hourly Weather Data.

Inspiration

What are the best forecasting models to address this particular problem? TBATS, ARIMA, Prophet? You tell me!

This dataset provides values for TEMPERATURE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This data release consists of Network Common Data Form (NetCDF) data sets of daily total-precipitation and minimum and maximum air temperatures for the time period from January 1, 1895 to December 31, 1915. These data sets are based on individual station data obtained for 153 National Oceanic and Atmospheric Administration (NOAA) weather stations in Florida and parts of Georgia, Alabama, and South Carolina (available at http://www.ncdc.noaa.gov/cdo-web/results). Weather station data were used to produce a total of 23,007 daily raster surfaces (7,669 daily raster surfaces for each of the 3 data sets) using a thin-plate-spline method of interpolation. The geographic extent of the weather station data coincides with the geographic extent of the Floridan aquifer system, with the exception of a small portion of southeast Mississippi where the Floridan aquifer system is saline and was not used.

Animals tend to decrease in body size (Bergmann’s rule) and elongate appendages (Allen’s rule) in warm climates. However, it is unknown whether these patterns depend on each other or constitute independent responses to thermal environment. Here, based on a global phylogenetic comparative analysis across 99.7% of the world’s bird species, we show that the way in which the relative length of unfeathered appendages co-varies with temperature depends on body size and vice versa. First, the larger the body, the greater the increase in beak length with temperature. Second, the temperature-based increase in tarsus length is apparent only in larger birds, whereas in smaller birds, tarsus length decreases with temperature. Third, body size and the length of beak and tarsus interact each other to predict the species’ temperature preferences. These findings suggest that the animals’ body size and shape are products of an evolutionary compromise that reflects distinct alternative thermoregulatory adaptations. Methods BirdTherm.xlsx The data was obtained by using sf (version 1.0-8)1 and raster (version 3.5-15)2 R packages. As input, we used global raster layers of temperatures World Clim (version 2.1)3. These rasters had a resolution of 30” and were consist of monthly averages from a period of 58 years (1960-2018). The temperature metrics have been calculated within polygons of species ranges available in form of multi-polygon vector layers extracted from the BirdLife International database (version 2020.1)4. First, we excluded polygons identified as uncertain species presence, uncertain season of presence, non-native presence or species extinct in a region, leaving us with 9,962 species (out of 9,993 species) with complete geographic data. Second, having polygons with only a certain, native and extant species presence we grouped them by the species (according to the phylogenetic taxonomy5) and the season of presence (either breeding season, winter or year-round presence) and then we aggregated them to obtain single polygons specific to species and season. Third, using breeding and year-round species ranges, where species live at hotter period of the year, we calculated their zonal means of monthly temperature maximums (Tmax3) and took the largest monthly value for each species (here, maximum temperature of all months, maxTmax). We also calculated their zonal means of monthly temperature averages (Tavg3) and took the largest monthly value for each species (here, average temperature of hottest month, maxTavg). Fourth, we analogously used winter and year-round species ranges, where species live at colder period of the year. Then, we calculated their zonal means of monthly temperature minimum (Tmin3) and took the lowest monthly value for each species (here, minimum temperature of all months, minTmin). We also calculated their zonal means of monthly temperature averages (Tavg3) and took the lowest monthly value for each species (here, average temperature of coldest month, minTavg). Fifth, we took all species ranges (breeding, winter and year-round, summarized to single polygon per species) and we calculated their zonal means of monthly temperature averages (Tavg3) and averaged all monthly values to obtain average temperature of all months (avgTavg) for each species. Note that this measure was accurate to describe conditions for resident species (which comprise of 79.9% of all species), but less for migrants, especially where their breeding and wintering ranges are on opposite hemispheres. Sixth, the obtained temperature measures: minimum tmperature of all months (minTmin), average temperature of coldest month (minTavg), average temperature of all months (avgTavg), average temperature of hottest month (maxTavg) and maximum temperature of all months (maxTmax) were transformed with two different formulas to normalize left-shewed distribution: tr2minTmin = -log10(max(x+1)-minTmin) tr2minTavg = -log10(max(x+1)-minTavg) tr2avgTavg = -log10(max(x+1)-avgTavg) tr1maxTavg = -log10(max(x+1)-maxTavg) tr1maxTmax = -log10(max(x+1)-maxTmax) Seventh, we also assessed geographic range size by aggregating all seasonal ranges of a species to a single polygon and calculated the area (in km2) on an equal-area cylindrical map projection (Eckert IV), ensuring comparable measurements from poles to equator. Eights, we have also extracted latitudinal and longitudinal data of the centroids of avian geographic ranges for visualization purposes. The temperature measures reflected the full range of global thermal environments occupied by birds. For example, the maximum temperature of all months ranged from -3.8 °C (in the emperor penguin Aptenodytes forsteri), through 29.9 °C (median, in the Minas gerais tyrannulet Phylloscartes roquettei) to 43.8 °C (in the Basra reed-warbler Acrocephalus griseldis). In contrast, the minimum temperature of all months ranged from -35.3 °C (in black-billed capercaillie Tetrao urogalloides), through 14.1 °C (median, e.g. in Yellow-breasted apalis Apalis flavida) to 24.8 °C (in the Seychelles warbler Acrocephalus sechellensis). Notably, our multiple measures of temperature indicated distinct aspects of seasonality in thermal conditions that may require different phenotypic adaptations across avian lineages. References 1. Pebesma, E. Simple Features for R: Standardized Support for Spatial Vector Data. R J. 10, 439–446 (2018). 2. Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3. 5–15 (2022). 3. Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017). 4. BirdLife International. Data Zone. Data Zone (2020). Available at: http://datazone.birdlife.org/, accessed in March 2021. 5. Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).

The table Global Temperatures by Country is part of the dataset Climate Change: Earth Surface Temperature Data, available at https://columbia.redivis.com/datasets/1e0a-f4931vvyg. It contains 577462 rows across 4 variables.

Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

Heat Wave Days are the number of days in the forecast period with a maximum temperature above the cardinal maximum temperature, the temperature at which crop growth ceases. This temperature is 35°C for warm season crops (dhw_warm). Week 1 and week 2 forecasted index is available daily from April 1 to October 31. Week 3 and week 4 forecasted index is available weekly (Thursday) from April 1 to October 31. Warm season crops require a relatively warm temperature condition. Typical examples include bean, soybean, corn and sweet potato. They normally grow during the summer season and early fall, then ripen in late fall in southern Canada only. Other agricultural regions in Canada do not always experience sufficiently long growing seasons for these plants to achieve maturity. The optimum temperature for such crops is 30°C. Agriculture and Agri-Food Canada (AAFC) and Environment and Climate Change Canada (ECCC) have together developed a suite of extreme agrometeorological indices based on four main categories of weather factors: temperature, precipitation, heat, and wind. The extreme weather indices are intended as short-term prediction tools and generated using ECCC’s medium range forecasts to create a weekly index product on a daily and weekly basis.

This feature layer contains the gridded one month PRISM Temperature Normals from Oregon State University on a 0.5 x 0.5 degree grid for the contiguous United States. The data was originally created in February 2018. These climatologies will be updated along with the drought outlook tools.The one month climatology has the same time period as the one month lead for the Climate Prediction Center's One Month Outlook. This climatology is for the current one month forecast released on the third Thursday of every month and updated on the last day of the month for the following month. This is a tool for the Drought Outlook Interactive Web Map and Drought Outlook Interactive Experience.The Climate Prediction Center uses climatologies with a base period from 1981 to 2010.For more information visit the PRISM Climate Group, Oregon State University, https://prism.oregonstate.edu

The table Global Temperatures by City is part of the dataset Climate Change: Earth Surface Temperature Data, available at https://columbia.redivis.com/datasets/1e0a-f4931vvyg. It contains 8599212 rows across 7 variables.