Data Methods: Extreme Heat Days & Warm Nights
The steps below describe how the values shown in the tool were calculated, from raw climate model output to the final data product.
Input data
Climate model simulations
- Description
ERA5 historical reanalysis data and ensemble of 4 bias-adjusted climate model simulations, dynamically downscaled using the WRF model for the state of California:
EC-Earth3-r1i1p1f1,MPI-ESM1-2-HR-r3i1p1f1,TaiESM1-r1i1p1f1,MIROC6-r1i1p1f1.- Variables
Maximum daily air temperature (extreme heat days), minimum daily air temperature (warm nights)
- Spatial resolution
3 kilometers
- Temporal resolution
Daily
- Time period
- WRF simulations: 1980-2100, the modeled historical period (1980-2014) followed by the SSP3-7.0 scenario (2015-2100)
- ERA5 reanalysis: 1987-2017
- Download
Browse the WRF simulations in the Cal-Adapt data catalog (STAC), or download directly from cloud storage (AWS).
Output data
Regional tables
- Description
The number of extreme heat days or warm nights per year for each threshold and global warming level, as the median, 10th percentile, and 90th percentile across simulations
- Format
One CSV table per region
- Download
Browse in the Cal-Adapt data catalog (STAC), or download directly from cloud storage (AWS).
Gridded data
- Description
The mean number of extreme heat days or warm nights per year at each grid cell, and the change from the historical baseline, for each threshold and global warming level, as statistics across simulations (median, 25th and 75th percentile, minimum, mean, and maximum)
- Shown in
- Format
Zarr stores at 3 kilometer resolution
- Download
Download directly from cloud storage (AWS).
Calculation steps
Data retrieval and preprocessing
Using Cal-Adapt's open source Python library
climakitae, input data was
retrieved. Climate model simulations were retrieved for historical and future
global warming levels (GWL): 0.8, 1.5, 2.0, 2.5, and 3.0 °C. This retrieval
process pulls the 30 year time window around the year (+/- 15 years) in which
the global warming level is reached. A 30 year window is used to look at typical
conditions over a long period centered on the global warming level. ERA5
historical reanalysis data was retrieved for 1987-2017, corresponding to GWL
0.8.
Inland water was masked from the data to discard artificially high values over water that are not representative of the variable.
Percentile-based threshold
For percentile-based thresholds, a per-gridcell historical temperature threshold was calculated using the 0.8 GWL for each simulation. This value represents an extreme heat threshold unique to the historical conditions at each location.
Number of exceedance days in each year
For each threshold (e.g. 95 °F, 100 °F, 99th percentile), the number of days in each year that the maximum (extreme heat days) or minimum (warm nights) daily temperature exceeds the threshold was counted. The exceedance days were calculated for each simulation and GWL. Absolute thresholds range from 80 to 135 °F for extreme heat days and from 65 to 135 °F for warm nights; percentile-based thresholds range from the 90th to the 99th percentile.
Median and variability over GWL time window
The following metrics were calculated for each simulation across the 30-year period corresponding to each GWL: median, 10th percentile, 90th percentile. This collapses the time dimension, yielding a single value per metric per simulation.
Change signal
For each GWL above the reference period (1.5 °C and higher) and for each metric (median, 10th percentile, 90th percentile), the historical baseline value was subtracted from the future GWL value. This difference represents the projected change in extreme heat days relative to the historical period.
Spatial aggregation
Next, we aggregated the gridded data by spatial unit (e.g. county, city, etc) by taking a median of all the grid cells within the boundary. A median is used to capture the typical number of extreme heat days within the area of interest without introducing bias from any anomalous grid cells.
Simulation aggregation
Finally, the data was aggregated across simulations. For the regional tables, the median, 10th percentile, and 90th percentile were taken across the four simulations' median values from step 4. The final data product is a table for each region containing the following variables:
- Median number of annual exceedance days across simulations (for each GWL and threshold)
- 10th percentile number of annual exceedance days across simulations (for each GWL and threshold)
- 90th percentile number of annual exceedance days across simulations (for each GWL and threshold)
The change signal from step 5 is carried into the gridded data.
Cal-Adapt. (2026). Data Methods: Extreme Heat Days & Warm Nights. Cal-Adapt. https://cal-adapt.org/dashboard/data-methods/extreme-heat-days