The North America climate data were derived from WorldClim, a set of global climate layers developed by the Museum of Vertebrate Zoology at the University of California, Berkeley, USA, in collaboration with The International Center for Tropical Agriculture and Rainforest CRC with support from NatureServe.The global climate data layers were generated through interpolation of average monthly climate data from weather stations across North America. The result is a 30-arc-second-resolution (1-Km) grid of mean temperature values. The North American data were clipped from the global data and reprojected to the standard Lambert Azimuthal Equal Area projection used for the North American Environmental Atlas. Background information on the WorldClim database is available in: Very High-Resolution Interpolated Climate Surfaces for Global Land Areas; Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis; International Journal of Climatology 25: 1965-1978; 2005.Files Download

A simulation of projected changes in mean annual precipitation from the period 1975 to 1995 to the period 2040 to 2060, is shown on this map. On average, precipitation increases, but it is not evenly distributed geographically. There are marked regions of decreasing, as well as increasing precipitation, over both land and ocean. Annual average precipitation generally increases over northern continents, and particularly during the winter. Warmer surface temperature would speed up the hydrological cycle at least partially, resulting in faster evaporation and more precipitation. The results are based on climate change simulations made with the Coupled Global Climate Model developed by Environment Canada.

The North American Climate Zones map shows the distribution of climate types across Canada, Mexico, and the United States based on the Köppen-Geiger climate classification.This map is derived from the global climate zones presented by Beck et al. (2018), “Present and future Köppen-Geiger climate classification maps at 1-km resolution,” and represents the spatial distribution in vector format of 29 climate zones (out of 30 global climate zones) present in North America.This map was produced by resampling the original input data spatial resolution of 0.0083 degrees to 0.016 degrees and cropping the global data to the North American region. The map was used to meet the needs of the CEC project “Improving the effectiveness of early warning systems for drought” in assessing the effectiveness of available drought indicators and indices in climate zones of North America.Reference:Beck, H., Zimmermann, N., McVicar, T. et al. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci Data 5, 180214 (2018). https://doi.org/10.1038/sdata.2018.214Files Download

In 2024, Louisiana recorded ***** inches of precipitation. This was the highest precipitation within the 48 contiguous U.S. states that year. On the other hand, Nevada was the driest state, with only **** inches of precipitation recorded. Precipitation across the United States Not only did Louisiana record the largest precipitation volume in 2024, but it also registered the highest precipitation anomaly that year, around 14.36 inches above the 1901-2000 annual average. In fact, over the last decade, rainfall across the United States was generally higher than the average recorded for the 20th century. Meanwhile, the driest states were located in the country's southwestern region, an area which – according to experts – will become even drier and warmer in the future. How does global warming affect precipitation patterns? Rising temperatures on Earth lead to increased evaporation which – ultimately – results in more precipitation. Since 1900, the volume of precipitation in the United States has increased at an average rate of **** inches per decade. Nevertheless, the effects of climate change on precipitation can vary depending on the location. For instance, climate change can alter wind patterns and ocean currents, causing certain areas to experience reduced precipitation. Furthermore, even if precipitation increases, it does not necessarily increase the water availability for human consumption, which might eventually lead to drought conditions.

The map shows the mean total precipitation in the month of October. October marks the transition from mainly rain to snowfall across northern Canada. Snowfall also occurs across much of the interior of southern Canada but in relatively small amounts that usually melt. October also marks the transition to the rainy season on the southern portion of British Columbia’s west coast.

A simulation of projected changes in December to February precipitation from the period 1975 to 1995 to the period 2040 to 2060 is shown on this map. On average, precipitation increases, but it is not evenly distributed geographically. There are marked regions of decreasing, as well as increasing precipitation, over both land and ocean. Annual average precipitation generally increases over northern continents, and particularly during the winter. Warmer surface temperature would speed up the hydrological cycle at least partially, resulting in faster evaporation and more precipitation. The results are based on climate change simulations made with the Coupled Global Climate Model developed by Environment Canada.

This map shows the projected change in mean precipitation for 2081-2100, with respect to the reference period of 1986-2005 for RCP2.6, expressed as a percentage (%) of mean precipitation in the reference period. The median projected change across the ensemble of CMIP5 climate models is shown.

For more maps on projected change, please visit the Canadian Climate Data and Scenarios (CCDS) site: http://ccds-dscc.ec.gc.ca/index.php?page=download-cmip5.

This dataset comprises a collection of modified secondary datasets that were used to construct the Weather and Climate Profiles for the FloodWise Communities project. The nature and source for each dataset is summarized below.

1. "Climate Trend Graph Data". Contains annual average temperature and rainfall for various regions of the Gulf Coast, regions defined by the Southern Climate Impacts Planning Program. Comma Separated (csv) temperature and precipitation data were downloaded for each region from 1895-2021, though only data values from 1950-2020 were used for the Weather and Climate Profiles. These data were used to create versions of SCIPP's Climate Trend Graphs that were bespoke to these Profiles, so that contrasts between regional and city/county-level climate trends in each Profile could be drawn.

2a. "Observations and Projections". Contains annually and seasonally averaged temperature and rainfall metrics for each recruited city/county for the FloodWise Communities project, as available through the South Central Applied Climate Information System (SC-ACIS). Observations from 1991 to 2020 of various temperature and precipitation metrics were extracted, in order to compute an historical average climate state, and an historical average climate change, for each recruited city/county. These data were prepared in csv format.

2b. "Observations and Projections". Contains annually and seasonally averaged temperature and rainfall metrics obtained from an ensemble of the GCM-RCMs that contributed to the North American CORDEX (NA-CORDEX) project. These climate model data were averaged over a Mid-Century (2041-2070) and an End-Century (2071-2100) period, in order to produce a range of projected changes in temperature and precipitation for each recruited city/county. The grid points from the NA-CORDEX's regular grid that were nearest to each recruited city/county were used to compute the same metrics as those using the SC-ACIS data, and saved to the same respective csv files. Each csv file was used to produce a table of historical and projected changes in temperature and precipitation, for inclusion in each recruited city/county's Weather and Climate Profile.

1. "FloodFactor Maps". Contains maps of categorical flood risk for each city/county recruited for the FloodWise Communities project, as produced by First Street Foundation. Permission was given by First Street Foundation to use and modify outputs from their FloodFactor product for the purpose of this project. FloodFactor maps for each recruited city/county were modified by circling properties at particular risk of being flooded, in order to draw the readers' attention to these properties.

2. "Sea Level Rise Maps". Contains maps of flooded land areas resulting from a potential 10 feet of sea level rise, as available in NOAA's Sea Level Rise Viewer. These maps were modified by circling particular areas of cities/counties that would be badly affected by this much sea level rise (e.g., bridges, properties surrounding river deltas, urban centers), again to draw the readers' attention to them.

3. "Severe Weather Tables". Contains tables of severe weather events that affected each city/county between 1991 and 2020, drawn from event summaries available in NOAA NCEI's Storm Events Database. Each table contains five specific weather events (e.g., hurricanes, thunderstorms, floods) that had high financial impact, and/or caused many human casualties, within this 30-year period. These tables serve to contextualize the types of severe weather that each recruited city/county has the potential to experience for its respective Profile.

Data describing clean growth and climate change projects that have received federal funding since 2015 that feeds into the Climate Action Map. The data include projects that meet Mitigation, Adaptation and Clean Technology objectives. The data include project names and descriptions, funding information, locations, and recipients.

The Precipitation Estimation from Remotely Sensed Information using an Artificial Neural Network-Climate Data Record (PERSIANN-CDR) is a new, retrospective satellite-based precipitation dataset, constructed as a climate data record for hydrological and climate studies. The PERSIANN-CDR is available from 1983-present making the dataset the longest satellite based precipitation data record available. The precipitation maps are available at daily temporal resolution for the latitude band 60°S–60°N at 0.25 degrees. The maps shown here represent 30-year annual and seasonal median and interquartile range (IQR) of the PERSIANN-CDR dataset from 1984 – 2014. In the median precipitation maps, the mid-point value (or 50th percentile) for each pixel in is computed and plotted for the study area. The range of the data about the median is represented by the interquartile range (IQR), and shows the variability of the dataset. For these maps, winter = December – February, spring = March – May, summer = June – August, fall = September – November

Additional file 1. ROSES for systematic map reports.

[ Derived from parent entry - See data hierarchy tab ]

The LANDMATE PFT dataset provides a land cover map for North America for the year 2015 in 0.1° (~10 km). The dataset is based on land cover data of the ESA Climate Change Initiative (ESA-CCI, native resolution: 300 m) which is translated into 16 plant functional types (PFTs) and non-vegetated classes employing the cross-walking procedure introduced by Reinhart et al. (2022), which was modified for North America. The translation is done under consideration of the Holdridge Life Zones (HLZs), a system that classifies land areas based on bioclimatic properties. Through the HLZs, regional distinction of the individual PFT distribution can be achieved. The land cover information is given as fractions per grid cell where each fraction represents the area covered by the respective land cover within each grid cell (0-1). The LANDMATE PFT dataset (0.1° resolution) serves as base map for the historical and future land use and land cover dataset LUCAS LUC developed by Hoffmann et al. (2023).

Plant functional types and non-vegetative classes: 1 - Tropical broadleaf evergreen trees 2 - Tropical deciduous trees 3 - Temperate broadleaf evergreen trees 4 - Temperate deciduous trees 5 - Evergreen coniferous trees 6 - Deciduous coniferous trees 7 - Coniferous shrubs 8 - Deciduous shrubs 9 - C3 grass 10 - C4 grass 11 - Tundra 12 - Swamp 13 - Non-irrigated crops 14 - Irrigated crops 15 - Urban 16 - Bare

Version 1.0 of this dataset does not exist. Here we use Version 1.1 in the name for consistency reasons.

RICCAR, MENA Region - PR - Annual Precipitation (RCM Ensemble for reference period) This raster dataset provides a representation of the Annual Precipitation (mm/month) in the Middle East and North Africa Region, for the reference period 1986-2005. The Regional Climate Model (RCM) ensemble adopts the time periods generally used by the Intergovernmental Panel for Climate Change (IPCC) and other regional climate modelling experiments. It runs climate simulations based on three future time periods that are compared with a historical reference period. The 50km pixel spatial resolution seasonal raster grids are available for Moderate and High representative concentration pathways (RCPs) RCP4.5/RCP8.5, climate change scenarios developed by IPCC for informing global and regional climate modelling work.

The LUCAS LUC future dataset consists of annual land use and land cover maps from 2016 to 2100 for North America. It is based on land cover data from the LANDMATE PFT dataset for the year 2015. The LANDMATE PFT consists of 16 plant functional types and non-vegetated classes that were converted from the ESA-CCI LC land cover data according to the method of Reinhart et al. (2022). For version 1.1 of the LUCAS LUC dataset, the improved LANDMATE PFT map version 1.1 was employed. The land use change information from the Land-Use Harmonization Data Set version 2 (LUH2 v2.1f, Hurtt et al. 2020) were imposed using the land use translator developed by Hoffmann et al. (2023). The projected land use change information was derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the 6th phase of Coupled Modelling Intercomparison Project (CMIP6). For each year, a map is provided that contains 16 fields. Each field holds the fraction the respective plant functional types and non-vegetated classes in the total grid cell (0-1). The LUCAS LUC dataset was constructed within the HICSS project LANDMATE and the WCRP flagship pilot study LUCAS to meet the requirements of downscaling experiments within CORDEX. Plant functional types and non-vegetative classes: 1 - Tropical broadleaf evergreen trees 2 - Tropical deciduous trees 3 - Temperate broadleaf evergreen trees 4 - Temperate deciduous trees 5 - Evergreen coniferous trees 6 - Deciduous coniferous trees 7 - Coniferous shrubs 8 - Deciduous shrubs 9 - C3 grass 10 - C4 grass 11 - Tundra 12 - Swamp 13 - Non-irrigated crops 14 - Irrigated crops 15 - Urban 16 - Bare

RICCAR, MENA Region - R10 - Annual Count of 10 mm precipitation days (GCM, 1951 to 2100) This raster dataset provides a representation of the heavy precipitation index Annual R10 - Annual count of 10mm precipitation days - number of days when daily precipitation ≥ 10mm - for the Middle East and North Africa Region, 1951-2100 period. The climate change projections use global climate models/global circulation models (GCMs). These are numerical models that combine physical processes on the land surface and in the ocean, atmosphere and cryosphere to simulate the response of the global climate system to increasing greenhouse-gas concentrations. The 50km pixel resolution raster grids are provided for three of the four representative concentration pathways (RCPs), climate change scenarios developed by IPCC for informing global and regional climate modelling work, namely; low, moderate and high (RCP2.6 /4.5 /8.5) and generated for each of the GCMs: - CNRM-CM5 System Model; - EC-EARTH System Model; - NOAA-GFDL-GFDL-ESM2M System Model.

A simulation of the projected changes in June to August precipitation from the period 1975 to 1995 to the period 2080 to 2100 is shown on this map. On average, precipitation increases, but it is not evenly distributed geographically. There are marked regions of decreasing, as well as increasing precipitation, over both land and ocean. Annual average precipitation generally increases over northern continents, and particularly during the winter. Warmer surface temperature would speed up the hydrological cycle at least partially, resulting in faster evaporation and more precipitation. The results are based on climate change simulations made with the Coupled Global Climate Model developed by Environment Canada.

Each annual file contains 35 metrics calculated by CANUE staff using base data provided by the Canadian Forest Service of Natural Resources Canada.The base data consist of interpolated daily maximum temperature, minimum temperature and total precipitation for all unique DMTI Spatial Inc. postal code locations in use at any time between 1983 and 2015. These were generated using thin-plate smoothing splines, as implemented in the ANUSPLIN climate modeling software. The earliest applications of thin-plate smoothing splines were described by Wahba and Wendelberger (1980) and Hutchinson and Bischof (1983), but the methodology has been further developed into an operational climate mapping tool at the ANU over the last 20 years. ANUSPLIN has become one of the leading technologies in the development of climate models and maps, and has been applied in North America and many regions around the world. ANUSPLIN is essentially a multidimensional “nonparametric” surface fitting method that has been found particularly well suited to the interpolation of various climate parameters, including daily maximum and minimum temperature, precipitation, and solar radiation.Equations for calculating the included metrics, based on daily minimum and maximum temperature, and total precipitation were developed by Pei-Ling Wang and Dr. Johannes Feddema at the University of Victoria, Geography Department, and implemented by CANUE staff Mahdi Shooshtari.

The LUCAS LUC historical dataset consists of annual land use and land cover maps from 1950 to 2015 for North America. It is based on land cover data from the LANDMATE PFT dataset that was generated from ESA-CCI LC data. The ESA-CCI LC land cover classes are converted into 16 plant functional types and non-vegetative classes employing the method of Reinhart et al. (2022). For version 1.1 of the LUCAS LUC dataset, the improved LANDMATE PFT map version 1.1 was employed. The land use change information from the Land-Use Harmonization Data Set version 2 (LUH2 v2h, Hurtt et al. 2020) were imposed using the land use translator developed by Hoffmann et al. (2023). For each year, a map is provided that contains 16 fields. Each field holds the fraction the respective plant functional types and non-vegetative classes in the total grid cell (0-1). The LUCAS LUC dataset was constructed within the HICSS project LANDMATE and the WCRP flagship pilot study LUCAS to meet the requirements of downscaling experiments within CORDEX. Plant functional types and non-vegetative classes: 1 - Tropical broadleaf evergreen trees 2 - Tropical deciduous trees 3 - Temperate broadleaf evergreen trees 4 - Temperate deciduous trees 5 - Evergreen coniferous trees 6 - Deciduous coniferous trees 7 - Coniferous shrubs 8 - Deciduous shrubs 9 - C3 grass 10 - C4 grass 11 - Tundra 12 - Swamp 13 - Non-irrigated crops 14 - Irrigated crops 15 - Urban 16 - Bare

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.

[ Derived from parent entry - See data hierarchy tab ]

The LUCAS LUC future dataset consists of annual land use and land cover maps from 2016 to 2100 for North America. It is based on land cover data from the LANDMATE PFT dataset for the year 2015. The LANDMATE PFT consists of 16 plant functional types and non-vegetated classes that were converted from the ESA-CCI LC land cover data according to the method of Reinhart et al. (2022). For version 1.1 of the LUCAS LUC dataset, the improved LANDMATE PFT map version 1.1 was employed. The land use change information from the Land-Use Harmonization Data Set version 2 (LUH2 v2.1f, Hurtt et al. 2020) were imposed using the land use translator developed by Hoffmann et al. (2023). The projected land use change information was derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the 6th phase of Coupled Modelling Intercomparison Project (CMIP6). For each year, a map is provided that contains 16 fields. Each field holds the fraction the respective plant functional types and non-vegetated classes in the total grid cell (0-1). The LUCAS LUC dataset was constructed within the HICSS project LANDMATE and the WCRP flagship pilot study LUCAS to meet the requirements of downscaling experiments within CORDEX. Plant functional types and non-vegetative classes: 1 - Tropical broadleaf evergreen trees 2 - Tropical deciduous trees 3 - Temperate broadleaf evergreen trees 4 - Temperate deciduous trees 5 - Evergreen coniferous trees 6 - Deciduous coniferous trees 7 - Coniferous shrubs 8 - Deciduous shrubs 9 - C3 grass 10 - C4 grass 11 - Tundra 12 - Swamp 13 - Non-irrigated crops 14 - Irrigated crops 15 - Urban 16 - Bare