Pima County Climate Report: September 2025
Highlights
- The current average 3-month (July-September) Standardized
Precipitation Index (SPI) for Pima County is -1.33 (Moderately Dry).
- Average July-September precipitation was 3.63 inches, which was -2.43 inches different from the long-term average. This value ranks 125th out of 131 years in total precipitation (Rank 1 is the wettest year).
- Average July-September temperature was 86 degrees F, which was +2.8 degrees F different from the long-term average. This value ranks 5th out of 131 years in average temperature (Rank 1 is the warmest year).
- The 1-month outlook for October predicts equal chances of above, below or normal precipitation and a 33-40% chance of warmer-than-average temperatures. The 3-month seasonal outlook for October-December predicts a 40-50% chance of drier-than-average precipitation and a 50-60% chance of warmer-than-average temperatures. (More information at NOAA Climate Prediction Center, https://www.cpc.ncep.noaa.gov/)
Pima County drought status for Summer 2025 (September 2025 3-Mo. SPI)
| County | Minimum SPI | Mean SPI | Maximum SPI | Total Precip [in.] | Anomaly [in.] |
|---|---|---|---|---|---|
| Pima County | -2.36 | -1.33 | 0.64 | 3.63 | -2.43 |
| Note: | |||||
| Pima CountySPI and climate statistics for Summer 2025 (July-September). Statistics are calculated based on the average of all PRISM grid cells lying within a County boundary. |
Seasonal Drought Progression
Pima County seasonal SPI values (2022-2025). Seasonal monthly definitions are as follows: Winter (Jan-Mar); Spring (Apr-Jun); Summer (Jul-Sep); Fall (Oct-Dec).The displayed SPI value represents the final month within each season at a timescale of the number of months within that season. Current SPI values are not final since the season is still in progress.
| County | 2025 Summer SPI* | 2025 Spring SPI | 2024 Winter SPI | 2024 Fall SPI | 12mo. SPI | 2025 Summer Precip [in.]* | 2025 Spring Precip [in.] | 2024 Winter Precip [in.] | 2024 Fall Precip [in.] | 12mo. Precip [in.] | 12mo. Precip Anom [in.] |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Pima County | -1.33 | 0.41 | -1.44 | -1.59 | -2.49 | 3.63 | 0.75 | 0.62 | 0.36 | 5.36 | -6.55 |
| Note: | |||||||||||
| Pima County seasonal SPI and climate statistics by County. Asterisk (*) signifies season in progress - currently partial Summer (Jul-Sep). Values are calculated based on the average of all PRISM grid cells lying within a County boundary. |
12mo. SPI for Pima County (2000-2025)
Station Climate Summaries
Summaries from climate stations with relatively long periods of record, minimal missing data (<10% of days), and within the area boundary are presented in the following tables (5 and 6) as reference locations. These stations are a select subset of stations that contribute to the gridded climate maps. Red circles on map indicate locations of NOAA Global Historical Climate Network stations.NOAA station(s) located within the report area boundary.
| Station | Elev (ft) | POR | Total Precip (in) | Precip Anom (in) | Days with Precip | Total Snow (in) | Avg Temp (F) | Temp Anom (F) | Freeze Days |
|---|---|---|---|---|---|---|---|---|---|
| TUCSON INTERNATIONAL AIRPORT | 2551 | 1949-2025 | 2.82 | -3.04 | 17 | 0 | 88.2 | 3.48 | 0 |
| SAGUARO NATIONAL PARK | 3080 | 2008-2025 | 3.73 | -2.77 | 19 | 0 | 87.0 | 1.88 | 0 |
| CATALINA STATE PARK | 2707 | 2007-2025 | 3.89 | -3.31 | 18 | 0 | 83.2 | 0.26 | 0 |
| ANVIL RANCH | 2759 | 1970-2025 | 3.15 | -3.05 | 15 | 0 | 84.8 | 3 | 0 |
| KARTCHNER CAVERNS | 4690 | 2008-2025 | 7.34 | -1.98 | 24 | 0 | 77.9 | 1.72 | 0 |
| GREEN VALLEY | 2900 | 2006-2025 | 6.27 | -1.3 | 22 | 0 | 85.2 | 1.15 | 0 |
| KITT PEAK | 6790 | 1992-2025 | 9.25 | 0.23 | 16 | 0 | 70.1 | 1.09 | 0 |
| SASABE | 3590 | 2009-2025 | 3.64 | -3.75 | 17 | 0 | 82.4 | 1.95 | 0 |
| AJO | 1751 | 1954-2025 | 1.98 | -1.48 | 9 | 0 | 92.3 | 3.34 | 0 |
| TUCSON 11 W | 2733 | 2003-2025 | 3.65 | -2.08 | 16 | 0 | 87.2 | 1.85 | 0 |
| Note: | |||||||||
| Pima County summary statistics of select NOAA stations within the County boundary for July-September 2025 |
NASA SPoRT Soil Moisture Estimates
Integrated surface to 2 meter deep relative soil moisture estimate
Modeled soil moisture estimates are provided by the NASA Short-term Prediction Research and Transition Center. This program uses a land surface model to integrate surface weather conditions (e.g. precipitation, temperature, wind…) with surface and soil properties like vegetation cover, soil depth and type to track and make near real-time estimates of soil moisture on a 3km by 3km grid. This map displays how unusually wet or dry the relative soil moisture (based on local soil properties) is for the integrated amount from the surface to 2 meters deep. (more information at https://weather.ndc.nasa.gov/sport/modeling/lis.html)
Climate Summary
Precipitation totals from the previous 12-months compared to the historical average for Pima County.
Mechanics Behind the Standardized Precipitation Index (SPI)
The SPI is a meteorological drought index which use monthly precipitation sums to calculate a time series of z-score values. The SPI uses z-score values to represent the number of standard deviations a monthly precipitation total is from the long-term mean. The sign (positive or negative) of a z-score value represents if the monthly total precipitation is above (+, water surplus) or below (-, water deficit) the long-term mean for all other instances of that month on record. Furthermore, the size of the z-score value represents the frequency of drought conditions (Table . Smaller SPI values (i.e. falling near zero) represent more frequent drought events while larger SPI values (positive or negative) are less frequent drought events.
| SPI Value | SPI Category |
|---|---|
| \(\geq\) 2 | Extremely Wet |
| 1.5 to 1.99 | Very Wet |
| 1 to 1.49 | Moderately Wet |
| -0.99 to 0.99 | Near Average |
| -1 to -1.49 | Moderately Dry |
| -1.5 to -1.99 | Very Dry |
| \(\leq\) -2 | Extremely Dry |
| Note: | |
| Table adapted from https://drought.unl.edu/Monitoring/SPI/MapInterpretation.aspx |
An important feature of the SPI is the ability to be calculated at a variety of monthly timescales. This flexibility allows the SPI to evaluate drought conditions for different time periods. For example, a 3-month SPI calculation compares total precipitation from the 3 months with all other instances of those same 3 months on record. Land managers can assess SPI values of different timescales to interpret short and long-term drought conditions on their land.
About the data used in this report
- PRISM Climate: The gridded used in mapping and forest and district level climate summaries is provided by the PRISM (Parameter elevation Regression on Independent Slopes Model) statistical mapping system. This system uses a weighted regression scheme to interpolate station data while accounting complexities like topography and rain shadows. The PRISM mapping system relies on a high density of stations to account for small variations in temperature and precipitation. Use caution in interpreting fine-scale patterns (or lack thereof) in regions with low station density. More information on PRISM can be found at https://prism.oregonstate.edu/ and https://climatedataguide.ucar.edu/climate-data/prism-high-resolution-spatial-climate-data-united-states-maxmin-temp-dewpoint.
- Climate Stations: Station-level data used in this report consist of NOAA Global Historical Climatology Network(NOAA-GHCN) stations and USDA NRCS Snow Telemetry sites which include Cooperative Observer sites, Airports, and CoCoRAHS volunteer observations and also USDA NRCS Snow Telemetry(SNOTEL) sites. NOAA-GHCN stations consist of Cooperative Observer sites, Airports, and CoCoRAHS volunteer precipitation observations. SNOTEL sites are automated stations located in key snow monitoring locations, often in forested locations. NOAA-GHCN data were accessed through the Regional Climate Center-Applied Climate Information System(RCC-ACIS) and SNOTEL data were downloaded using the ‘snotelr’ package.
Contact information
Direct any questions, comments, or suggestions to:
- Mike Crimmins, Professor and Extension Specialist (crimmins@arizona.edu)
- Trevor McKellar, Research Scientist (tmckella@arizona.edu)
https://cals.arizona.edu/climate
