Try Wonders for freeEl Niño 2026 in Texas: what it could do in your county, and what to do
In 1997–98, 236 of 254 Texas counties got more December–February rain than normal, and the median one got 146%. In 2015–16, 64 did, and the median got 86% (Vose et al., 2014b). Here is every county, and a plan for yours.
By Wonders · September 24, 2026 · Data retrieved September 24, 2026
NOAA puts it at three chances in four (75%) that by October–December this is the strongest El Niño in its index record, which goes back to 1950 (Climate Prediction Center, 2026b). A new National Flood Insurance Program policy usually takes effect 30 days after you buy it, so buy it at least a month before the storms (FloodSmart, n.d.).
The full story, the research behind it and who is working on it now are in the main post.
Make your plan
Texas, county by county
How many of the seven strongest El Niño winters since 1950 brought heavier winter rain than each county’s 1991–2020 normal (Climate Prediction Center, 2026a; Vose et al., 2014b); heavier rain is what came with the flood and storm disasters listed below. 130 of 254 were wetter in five or more; 11 in two or fewer.
- 0–1 of 7 lighter winter rain in most
- 2 of 7 lighter more often
- 3–4 of 7 no clear pattern
- 5 of 7 heavier winter rain more often
- 6–7 of 7 heavier winter rain in most
Federal disasters declared in those winters
In the 6 strong El Niño winters since 1965–66, 6 federal weather disaster declarations, recorded by FEMA, covered 131 of Texas's 254 counties (Federal Emergency Management Agency, 2026). Across all its counties, that is 172 designations in 6 winters, or 28.7 a winter, against 14.3 a winter in the other 55.
- 1972–73High winds, tornadoes and flooding DR-365Severe Storm · 5 counties
- 1991–92Severe thunderstorms DR-930Flood · 65 counties
- 1991–92Severe storms & flooding DR-937Flood · 3 counties
- 2015–16Severe winter storms, tornadoes, straight-line winds, and flooding DR-4255Severe Storm · 51 counties
- 2015–16Severe storms, tornadoes, and flooding DR-4266Flood · 21 counties
- 2015–16Severe storms and flooding DR-4269Flood · 27 counties
A declaration means damage large enough for federal aid, so these undercount smaller floods and storms. Declarations have also become more common over the decades, which weighs the comparison toward the recent winters.
What to do before the storms
- A new National Flood Insurance Program policy usually takes effect 30 days after you buy it, so buy it at least a month before the storms (FloodSmart, n.d.).
- Six inches of fast-moving water can knock over an adult, and 12 inches can carry away most cars. More than half of flood drownings happen when a vehicle is driven into flood water (National Weather Service, n.d.-c).
- Before the fire season, clear leaves, debris and anything flammable for at least 30 feet around your home (Ready.gov, n.d.-c).
- After a wildfire, flood risk stays significantly higher for up to five years, until plants grow back (Federal Emergency Management Agency, n.d.).
Every county
December–February rain as a share of the 1991–2020 normal, and federal weather disaster designations in the strong winters since 1965–66.
| County | Wetter (of 7) | 97–98 | 15–16 | FEMA |
|---|---|---|---|---|
| Anderson | 3 | 131% | 95% | 2 |
| Andrews | 5 | 129% | 98% | — |
| Angelina | 5 | 146% | 80% | 1 |
| Aransas | 4 | 105% | 54% | 1 |
| Archer | 5 | 197% | 104% | — |
| Armstrong | 3 | 224% | 88% | — |
| Atascosa | 5 | 186% | 97% | — |
| Austin | 5 | 130% | 88% | 2 |
| Bailey | 5 | 163% | 95% | 1 |
| Bandera | 5 | 173% | 82% | — |
| Bastrop | 6 | 118% | 78% | 2 |
| Baylor | 5 | 197% | 120% | — |
| Bee | 5 | 125% | 106% | — |
| Bell | 6 | 204% | 59% | 1 |
| Bexar | 5 | 173% | 83% | — |
| Blanco | 6 | 175% | 65% | — |
| Borden | 4 | 135% | 55% | 1 |
| Bosque | 2 | 175% | 80% | 2 |
| Bowie | 3 | 136% | 119% | — |
| Brazoria | 5 | 156% | 67% | 1 |
| Brazos | 5 | 140% | 99% | 1 |
| Brewster | 4 | 65% | 68% | — |
| Briscoe | 3 | 211% | 84% | — |
| Brooks | 6 | 117% | 99% | — |
| Brown | 4 | 160% | 67% | 1 |
| Burleson | 5 | 139% | 90% | 1 |
| Burnet | 6 | 207% | 62% | 2 |
| Caldwell | 5 | 120% | 68% | — |
| Calhoun | 5 | 107% | 72% | 1 |
| Callahan | 4 | 140% | 71% | 2 |
| Cameron | 6 | 105% | 61% | — |
| Camp | 4 | 130% | 131% | — |
| Carson | 4 | 185% | 123% | — |
| Cass | 3 | 134% | 99% | 3 |
| Castro | 5 | 212% | 91% | 1 |
| Chambers | 4 | 181% | 78% | — |
| Cherokee | 4 | 125% | 97% | 1 |
| Childress | 4 | 247% | 101% | 1 |
| Clay | 4 | 205% | 98% | — |
| Cochran | 5 | 155% | 121% | 1 |
| Coke | 4 | 90% | 71% | — |
| Coleman | 5 | 126% | 67% | 1 |
| Collin | 4 | 190% | 110% | — |
| Collingsworth | 4 | 205% | 103% | 1 |
| Colorado | 5 | 131% | 92% | 1 |
| Comal | 5 | 176% | 62% | 1 |
| Comanche | 4 | 165% | 59% | 1 |
| Concho | 5 | 101% | 72% | — |
| Cooke | 4 | 167% | 114% | — |
| Coryell | 3 | 210% | 62% | 2 |
| Cottle | 5 | 216% | 104% | 1 |
| Crane | 5 | 117% | 92% | — |
| Crockett | 4 | 96% | 87% | — |
| Crosby | 5 | 146% | 110% | 1 |
| Culberson | 6 | 123% | 68% | — |
| Dallam | 4 | 191% | 63% | — |
| Dallas | 3 | 180% | 100% | 2 |
| Dawson | 4 | 146% | 61% | — |
| Deaf Smith | 4 | 192% | 84% | 1 |
| Delta | 4 | 161% | 127% | 1 |
| Denton | 3 | 175% | 103% | — |
| DeWitt | 5 | 136% | 86% | 1 |
| Dickens | 5 | 152% | 117% | 1 |
| Dimmit | 5 | 100% | 57% | — |
| Donley | 3 | 204% | 86% | 1 |
| Duval | 6 | 113% | 80% | — |
| Eastland | 3 | 145% | 64% | 1 |
| Ector | 5 | 136% | 75% | — |
| Edwards | 7 | 133% | 110% | — |
| El Paso | 6 | 136% | 95% | — |
| Ellis | 2 | 176% | 99% | 1 |
| Erath | 3 | 159% | 60% | 1 |
| Falls | 3 | 177% | 91% | — |
| Fannin | 4 | 186% | 120% | — |
| Fayette | 5 | 124% | 91% | 2 |
| Fisher | 4 | 142% | 74% | 1 |
| Floyd | 5 | 183% | 103% | 1 |
| Foard | 5 | 212% | 126% | 1 |
| Fort Bend | 5 | 136% | 66% | 2 |
| Franklin | 4 | 140% | 146% | 1 |
| Freestone | 4 | 165% | 101% | 1 |
| Frio | 7 | 162% | 111% | — |
| Gaines | 5 | 134% | 86% | — |
| Galveston | 4 | 181% | 73% | — |
| Garza | 4 | 140% | 81% | — |
| Gillespie | 6 | 176% | 72% | 1 |
| Glasscock | 4 | 96% | 57% | — |
| Goliad | 5 | 132% | 86% | — |
| Gonzales | 5 | 132% | 83% | 1 |
| Gray | 4 | 167% | 104% | — |
| Grayson | 3 | 181% | 123% | — |
| Gregg | 3 | 129% | 92% | 1 |
| Grimes | 4 | 140% | 91% | 1 |
| Guadalupe | 5 | 139% | 76% | — |
| Hale | 5 | 183% | 85% | — |
| Hall | 4 | 243% | 108% | 1 |
| Hamilton | 3 | 164% | 61% | 1 |
| Hansford | 4 | 160% | 87% | — |
| Hardeman | 5 | 227% | 132% | 1 |
| Hardin | 5 | 153% | 77% | — |
| Harris | 4 | 158% | 79% | 2 |
| Harrison | 3 | 134% | 83% | 3 |
| Hartley | 4 | 227% | 68% | — |
| Haskell | 5 | 162% | 103% | 1 |
| Hays | 5 | 171% | 66% | 1 |
| Hemphill | 3 | 132% | 88% | — |
| Henderson | 4 | 155% | 111% | 3 |
| Hidalgo | 6 | 135% | 50% | — |
| Hill | 2 | 196% | 86% | 2 |
| Hockley | 6 | 158% | 115% | 1 |
| Hood | 2 | 160% | 74% | 2 |
| Hopkins | 4 | 149% | 134% | 1 |
| Houston | 4 | 130% | 86% | 1 |
| Howard | 5 | 117% | 50% | — |
| Hudspeth | 6 | 113% | 76% | — |
| Hunt | 4 | 169% | 112% | — |
| Hutchinson | 5 | 178% | 108% | — |
| Irion | 4 | 84% | 76% | — |
| Jack | 3 | 179% | 92% | — |
| Jackson | 5 | 121% | 70% | — |
| Jasper | 5 | 150% | 78% | 1 |
| Jeff Davis | 4 | 90% | 54% | — |
| Jefferson | 4 | 152% | 73% | — |
| Jim Hogg | 6 | 117% | 74% | — |
| Jim Wells | 6 | 114% | 83% | — |
| Johnson | 2 | 173% | 88% | 1 |
| Jones | 4 | 134% | 87% | 3 |
| Karnes | 5 | 131% | 94% | — |
| Kaufman | 4 | 165% | 103% | 1 |
| Kendall | 5 | 179% | 66% | — |
| Kenedy | 6 | 139% | 91% | — |
| Kent | 4 | 145% | 96% | 1 |
| Kerr | 6 | 165% | 82% | 1 |
| Kimble | 6 | 134% | 80% | — |
| King | 5 | 187% | 103% | 1 |
| Kinney | 6 | 100% | 112% | — |
| Kleberg | 6 | 121% | 86% | — |
| Knox | 5 | 192% | 123% | 1 |
| La Salle | 6 | 128% | 85% | — |
| Lamar | 4 | 166% | 127% | 1 |
| Lamb | 5 | 164% | 81% | 1 |
| Lampasas | 3 | 186% | 66% | 1 |
| Lavaca | 5 | 128% | 93% | — |
| Lee | 6 | 132% | 80% | — |
| Leon | 4 | 140% | 91% | 2 |
| Liberty | 4 | 149% | 79% | 3 |
| Limestone | 3 | 179% | 92% | 3 |
| Lipscomb | 6 | 137% | 125% | — |
| Live Oak | 6 | 128% | 117% | — |
| Llano | 6 | 186% | 62% | 1 |
| Loving | 6 | 108% | 110% | — |
| Lubbock | 4 | 167% | 88% | 1 |
| Lynn | 4 | 147% | 70% | — |
| Madison | 3 | 128% | 88% | 2 |
| Marion | 3 | 133% | 91% | 1 |
| Martin | 5 | 122% | 82% | — |
| Mason | 6 | 151% | 61% | 1 |
| Matagorda | 5 | 122% | 69% | 1 |
| Maverick | 5 | 69% | 77% | — |
| McCulloch | 5 | 150% | 68% | — |
| McLennan | 2 | 201% | 77% | 2 |
| McMullen | 7 | 128% | 114% | — |
| Medina | 6 | 166% | 87% | — |
| Menard | 5 | 119% | 81% | — |
| Midland | 5 | 128% | 79% | — |
| Milam | 5 | 152% | 77% | 2 |
| Mills | 4 | 163% | 65% | 1 |
| Mitchell | 5 | 117% | 49% | — |
| Montague | 2 | 184% | 89% | — |
| Montgomery | 5 | 151% | 82% | 1 |
| Moore | 4 | 194% | 91% | — |
| Morris | 4 | 131% | 128% | — |
| Motley | 5 | 201% | 120% | 1 |
| Nacogdoches | 4 | 138% | 81% | — |
| Navarro | 4 | 181% | 106% | 2 |
| Newton | 5 | 148% | 73% | 1 |
| Nolan | 5 | 108% | 51% | 1 |
| Nueces | 5 | 108% | 69% | — |
| Ochiltree | 4 | 161% | 91% | — |
| Oldham | 5 | 197% | 75% | — |
| Orange | 5 | 144% | 71% | 1 |
| Palo Pinto | 3 | 145% | 63% | 1 |
| Panola | 4 | 142% | 75% | — |
| Parker | 2 | 163% | 72% | 3 |
| Parmer | 5 | 186% | 90% | 1 |
| Pecos | 5 | 90% | 110% | — |
| Polk | 6 | 163% | 81% | 1 |
| Potter | 3 | 215% | 89% | — |
| Presidio | 4 | 75% | 56% | — |
| Rains | 4 | 141% | 128% | 1 |
| Randall | 4 | 221% | 86% | — |
| Reagan | 4 | 86% | 67% | — |
| Real | 6 | 147% | 96% | — |
| Red River | 4 | 147% | 141% | 2 |
| Reeves | 4 | 95% | 66% | — |
| Refugio | 4 | 105% | 68% | 1 |
| Roberts | 3 | 158% | 87% | — |
| Robertson | 5 | 158% | 105% | 1 |
| Rockwall | 4 | 167% | 100% | 1 |
| Runnels | 5 | 103% | 67% | — |
| Rusk | 3 | 129% | 83% | 1 |
| Sabine | 4 | 141% | 74% | 1 |
| San Augustine | 5 | 147% | 74% | 1 |
| San Jacinto | 6 | 137% | 85% | 2 |
| San Patricio | 4 | 104% | 68% | — |
| San Saba | 4 | 164% | 66% | — |
| Schleicher | 6 | 107% | 112% | — |
| Scurry | 4 | 153% | 62% | 1 |
| Shackelford | 4 | 147% | 83% | 1 |
| Shelby | 4 | 145% | 79% | 1 |
| Sherman | 4 | 170% | 70% | — |
| Smith | 4 | 139% | 113% | 2 |
| Somervell | 2 | 163% | 82% | 1 |
| Starr | 6 | 114% | 54% | — |
| Stephens | 3 | 158% | 74% | — |
| Sterling | 4 | 84% | 60% | — |
| Stonewall | 5 | 162% | 100% | 1 |
| Sutton | 7 | 115% | 111% | — |
| Swisher | 3 | 208% | 83% | — |
| Tarrant | 2 | 178% | 95% | 1 |
| Taylor | 5 | 117% | 68% | — |
| Terrell | 3 | 97% | 86% | — |
| Terry | 6 | 136% | 118% | 1 |
| Throckmorton | 5 | 167% | 108% | 1 |
| Titus | 4 | 136% | 144% | 1 |
| Tom Green | 4 | 87% | 90% | — |
| Travis | 4 | 159% | 68% | 1 |
| Trinity | 4 | 145% | 86% | 2 |
| Tyler | 6 | 165% | 80% | 2 |
| Upshur | 4 | 134% | 117% | 1 |
| Upton | 5 | 117% | 85% | — |
| Uvalde | 7 | 126% | 107% | — |
| Val Verde | 6 | 104% | 96% | — |
| Van Zandt | 4 | 141% | 128% | 2 |
| Victoria | 4 | 111% | 79% | 1 |
| Walker | 3 | 132% | 91% | 3 |
| Waller | 5 | 149% | 84% | 1 |
| Ward | 5 | 116% | 83% | — |
| Washington | 5 | 123% | 89% | 1 |
| Webb | 5 | 92% | 72% | — |
| Wharton | 5 | 114% | 65% | 2 |
| Wheeler | 3 | 169% | 95% | 1 |
| Wichita | 5 | 229% | 112% | — |
| Wilbarger | 5 | 236% | 150% | 1 |
| Willacy | 6 | 157% | 60% | — |
| Williamson | 6 | 168% | 60% | 1 |
| Wilson | 5 | 140% | 89% | — |
| Winkler | 5 | 123% | 89% | — |
| Wise | 2 | 181% | 93% | 1 |
| Wood | 4 | 132% | 138% | 1 |
| Yoakum | 5 | 132% | 94% | — |
| Young | 3 | 191% | 97% | 1 |
| Zapata | 5 | 94% | 67% | — |
| Zavala | 7 | 111% | 108% | 1 |
National Weather Service offices
The offices that issue forecasts and warnings for Texas (National Weather Service, n.d.-a):
- NWS Lubbock, TX806-745-3916
- NWS Midland/Odessa(432) 563-5006
- NWS San Angelo, TX325-944-0526
- NWS Amarillo, TX(806) 335-1121
- NWS Fort Worth/Dallas, TX817.429.2631
- NWS Austin/San Antonio, TX(830) 629-0130
- NWS Corpus Christi, TX(361) 289-0959
- NWS El Paso, TX(575) 589-4088
- NWS Norman, OK(405) 325-3816
- NWS Houston/Galveston, TX281-337-5074
- NWS Shreveport, LA318-631-3669
- NWS Lake Charles, LA(337) 477-5285 M-F 8a to 4p only
- NWS Brownsville/Rio Grande Valley, TX956-504-1432 (8 AM to 430 PM Mon-Fri)
Questions
What can a strong El Niño winter do in Texas?
In the 6 strong El Niño winters since 1965–66, 6 federal weather disaster declarations (recorded by FEMA) covered Texas, including 131 of its 254 counties: High winds, tornadoes and flooding (1972–73); Severe thunderstorms (1991–92); Severe storms & flooding (1991–92); Severe winter storms, tornadoes, straight-line winds, and flooding (2015–16) and more.
Does El Niño bring more rain to Texas?
In the seven strongest El Niño winters since 1950, 130 of Texas's 254 counties with a full record got more December–February rain than their 1991–2020 normal in at least five of the seven, and 11 in two or fewer. The rest, 113, show no consistent pattern. Heavier winter rain came with the flood and storm disasters listed on this page, and a new National Flood Insurance Program policy usually takes 30 days to start.
What did the 1997–98 El Niño do in Texas?
236 of 254 counties got more December–February rain than normal; the median county got 146% of normal.
How did the 2015–16 El Niño compare?
64 of 254 counties got more rain than normal, and the median county got 86% of normal, against 146% in 1997–98. A strong El Niño does not tell you which winter you will get, so prepare for the wet one: a new flood insurance policy usually takes 30 days to start.
Which National Weather Service offices cover Texas?
NWS Lubbock, TX, NWS Midland/Odessa, NWS San Angelo, TX, NWS Amarillo, TX, NWS Fort Worth/Dallas, TX, NWS Austin/San Antonio, TX, NWS Corpus Christi, TX, NWS El Paso, TX, NWS Norman, OK, NWS Houston/Galveston, TX, NWS Shreveport, LA, NWS Lake Charles, LA, NWS Brownsville/Rio Grande Valley, TX. Enter a ZIP code above to find the one for your address.
How this page was made
Rain is NOAA’s monthly county series (Vose et al., 2014a, 2014b): December–February totals in the seven winters when NOAA’s relative Niño index was 1.5 °C or more (Climate Prediction Center, 2026a), against the 1991–2020 mean. Disasters are FEMA’s county designations for weather incidents beginning November to April, counted from 1965–66 (Federal Emergency Management Agency, 2026). Names are the Census Bureau’s (U.S. Census Bureau, 2020); offices are the National Weather Service’s county zones (National Weather Service, n.d.-a). Seven winters is a small sample, and the one ahead is forecast to be stronger than any of them (Climate Prediction Center, 2026b). Retrieved September 24, 2026.
References
- Climate Prediction Center. (2026a). Relative Oceanic Niño Index (RONI) [Dataset]. National Oceanic and Atmospheric Administration. https://www.cpc.ncep.noaa.gov/data/indices/RONI.ascii.txt
- Climate Prediction Center. (2026b, September 10). El Niño/Southern Oscillation (ENSO) diagnostic discussion. National Oceanic and Atmospheric Administration. https://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_advisory/ensodisc.shtml
- Federal Emergency Management Agency. (n.d.). Flood after fire: The increased risk. Ready.gov. Retrieved https://www.ready.gov/sites/default/files/Flood_After_Fire_Fact_Sheet.pdf
- Federal Emergency Management Agency. (2026). OpenFEMA dataset: Disaster Declarations Summaries - v2 [Dataset]. https://www.fema.gov/openfema-data-page/disaster-declarations-summaries-v2
- FloodSmart. (n.d.). Buy a policy. Federal Emergency Management Agency. Retrieved September 24, 2026, from https://www.floodsmart.gov/get-insured/buy-a-policy
- National Climatic Data Center. (1998). Service Assessment and Report: The winter of 1997–1998 severe storms (Technical Report 98-02). NOAA National Climatic Data Center. https://www.ncei.noaa.gov/monitoring-content/billions/reports/19971201-19980228-severe-storm/tr9802.pdf
- National Weather Service. (n.d.-a). API web service. National Oceanic and Atmospheric Administration. Retrieved September 24, 2026, from https://www.weather.gov/documentation/services-web-api
- National Weather Service. (n.d.-b). NOAA Weather Radio. National Oceanic and Atmospheric Administration. Retrieved September 24, 2026, from https://www.weather.gov/nwr/
- National Weather Service. (n.d.-c). Turn Around Don’t Drown. National Oceanic and Atmospheric Administration. Retrieved September 24, 2026, from https://www.weather.gov/safety/flood-turn-around-dont-drown
- National Weather Service. (n.d.-d). Wireless Emergency Alerts. National Oceanic and Atmospheric Administration. Retrieved September 24, 2026, from https://www.weather.gov/wrn/wea
- Ready.gov. (n.d.-a). Financial preparedness. U.S. Department of Homeland Security. Retrieved September 24, 2026, from https://www.ready.gov/financial-preparedness
- Ready.gov. (n.d.-b). Tornadoes. U.S. Department of Homeland Security. Retrieved September 24, 2026, from https://www.ready.gov/tornadoes
- Ready.gov. (n.d.-c). Wildfires. U.S. Department of Homeland Security. Retrieved September 24, 2026, from https://www.ready.gov/wildfires
- Ready.gov. (n.d.-d). Winter weather. U.S. Department of Homeland Security. Retrieved September 24, 2026, from https://www.ready.gov/winter-weather
- U.S. Census Bureau. (2020). 2020 FIPS codes for counties and county equivalent entities [Dataset]. https://www2.census.gov/geo/docs/reference/codes2020/national_county2020.txt
- U.S. Census Bureau. (2024). 2024 Gazetteer files: ZIP Code Tabulation Areas [Dataset]. https://www.census.gov/geographies/reference-files/time-series/geo/gazetteer-files.html
- Vose, R. S., Applequist, S., Squires, M., Durre, I., Menne, M. J., Williams, C. N., Fenimore, C., Gleason, K., & Arndt, D. (2014a). Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions. Journal of Applied Meteorology and Climatology, 53(5), 1232–1251. https://doi.org/10.1175/jamc-d-13-0248.1
- Vose, R. S., Applequist, S., Squires, M., Durre, I., Menne, M. J., Williams, C. N., Fenimore, C., Gleason, K., & Arndt, D. (2014b). NOAA Monthly U.S. Climate Divisional Database (NClimDiv) [Dataset]. NOAA National Centers for Environmental Information. https://doi.org/10.7289/V5M32STR
- Ward, A. (2026, September 16). Extreme meteorology (El Niño) with Dr. Marshall Shepherd [Audio podcast episode]. In Ologies with Alie Ward. https://www.alieward.com/ologies/extrememeteorology
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