Climatch

Land with a climate like Daxi

🇹🇼Daxi, Taiwan, Taiwan

The towns with the closest growing climate to Daxi are Amami, Japan (94% match), Tsuha and Ginowan. About 148,000 sq mi of land in 7 countries matches 80% or better, most of it in Guangdong, China, Guangxi, China and Louisiana, United States.The towns with the closest growing climate to Daxi are Amami, Japan (94% match), Tsuha and Ginowan. About 383,000 km² of land in 7 countries matches 80% or better, most of it in Guangdong, China, Guangxi, China and Louisiana, United States.

Your growing climate

Zone 11a · No frost · 4 chill hours · 89 in rainZone 11a · No frost · 4 chill hours · 2,258 mm rain

What grows in Daxi →

Regions with the most land like yours

  1. 1

    🇨🇳Guangdong, China

    44k sq mi114k km² matching 80% or better

    For example Anbu (87), Chenghua (86)

  2. 2

    🇨🇳Guangxi, China

    26k sq mi68k km² matching 80% or better

    For example Naliang (84), Shangsi (83)

  3. 3

    🇺🇸Louisiana, United States

    10k sq mi26k km² matching 80% or better

    For example Bayou Cane (84), Houma (84)

  4. 4

    🇧🇷São Paulo, Brazil

    9,300 sq mi24k km² matching 80% or better

    For example Iguape (90), Itariri (88)

  5. 5

    🇹🇼Taiwan

    8,700 sq mi23k km² matching 80% or better

    For example Xiulin (94), Puli (90)

  6. 6

    🇯🇵Okinawa, Japan

    7,500 sq mi20k km² matching 80% or better

    For example Yomitan (94), Tsuha (94)

  7. 7

    🇧🇷Santa Catarina, Brazil

    5,900 sq mi15k km² matching 80% or better

    For example Itapoá (89), São Francisco do Sul (89)

  8. 8

    🇨🇳Fujian, China

    4,100 sq mi11k km² matching 80% or better

    For example Tantou (83), Fuqing (83)

  9. 9

    🇺🇸Texas, United States

    3,900 sq mi10k km² matching 80% or better

    For example Lake Jackson (83), Texas City (82)

  10. 10

    🇧🇷Rio Grande do Sul, Brazil

    3,900 sq mi10k km² matching 80% or better

    For example Porto Alegre (84), Canoas (83)

  11. 11

    🇯🇵Kagoshima, Japan

    2,900 sq mi7,500 km² matching 80% or better

    For example Amami (94), Naze (93)

  12. 12

    🇺🇸Mississippi, United States

    2,100 sq mi5,400 km² matching 80% or better

    For example Pascagoula (81), Biloxi (81)

Regions are states and provinces, counting land within 75 km of a town. At most three per country, so the list covers the world.

Closest towns

  1. 1

    🇯🇵AmamiKagoshima, Japan

    Zone 11a · No frost · 2 chill hours · 100 in rainZone 11a · No frost · 2 chill hours · 2,540 mm rain

    Shares 23 of your 23 great-fit crops

    94%match
  2. 2

    🇯🇵TsuhaOkinawa, Japan

    Zone 11b · No frost · 0 chill hours · 86 in rainZone 11b · No frost · 0 chill hours · 2,177 mm rain

    Shares 23 of your 23 great-fit crops

    94%match
  3. 3

    🇯🇵GinowanOkinawa, Japan

    Zone 11b · No frost · 0 chill hours · 86 in rainZone 11b · No frost · 0 chill hours · 2,194 mm rain

    Shares 23 of your 23 great-fit crops

    93%match
  4. 4

    🇯🇵NahaOkinawa, Japan

    Zone 11b · No frost · 0 chill hours · 86 in rainZone 11b · No frost · 0 chill hours · 2,182 mm rain

    Shares 23 of your 23 great-fit crops

    92%match
  5. 5

    🇹🇼PuliTaiwan, Taiwan

    Zone 11a · No frost · 0 chill hours · 97 in rainZone 11a · No frost · 0 chill hours · 2,456 mm rain

    Shares 22 of your 23 great-fit crops

    90%match
  6. 6

    🇧🇷Iguape↻Seasons flipped. São Paulo, Brazil

    Zone 11b · No frost · 0 chill hours · 77 in rainZone 11b · No frost · 0 chill hours · 1,965 mm rain

    Shares 23 of your 23 great-fit crops

    90%match
  7. 7

    🇹🇼Hualien CityTaiwan, Taiwan

    Zone 11b · No frost · 0 chill hours · 82 in rainZone 11b · No frost · 0 chill hours · 2,087 mm rain

    Shares 23 of your 23 great-fit crops · Also great there: cacao

    90%match
  8. 8

    🇧🇷Matinhos↻Seasons flipped. Paraná, Brazil

    Zone 11b · No frost · 0 chill hours · 80 in rainZone 11b · No frost · 0 chill hours · 2,031 mm rain

    Shares 22 of your 23 great-fit crops

    89%match
  9. 9

    🇧🇷Paranaguá↻Seasons flipped. Paraná, Brazil

    Zone 11b · No frost · 0 chill hours · 81 in rainZone 11b · No frost · 0 chill hours · 2,050 mm rain

    Shares 22 of your 23 great-fit crops

    88%match
  10. 10

    🇯🇵MiyakojimaOkinawa, Japan

    Zone 11b · No frost · 0 chill hours · 86 in rainZone 11b · No frost · 0 chill hours · 2,188 mm rain

    Shares 23 of your 23 great-fit crops · Also great there: cacao

    88%match
  11. 11

    🇧🇷Santos↻Seasons flipped. São Paulo, Brazil

    Zone 12a · No frost · 0 chill hours · 100 in rainZone 12a · No frost · 0 chill hours · 2,541 mm rain

    Shares 23 of your 23 great-fit crops · Also great there: cacao

    87%match
  12. 12

    🇧🇷Caraguatatuba↻Seasons flipped. São Paulo, Brazil

    Zone 11b · No frost · 0 chill hours · 85 in rainZone 11b · No frost · 0 chill hours · 2,162 mm rain

    Shares 23 of your 23 great-fit crops

    87%match
  13. 13

    🇧🇷Joinville↻Seasons flipped. Santa Catarina, Brazil

    Zone 11a · No frost · 0 chill hours · 71 in rainZone 11a · No frost · 0 chill hours · 1,800 mm rain

    Shares 22 of your 23 great-fit crops

    87%match
  14. 14

    🇯🇵IshigakiOkinawa, Japan

    Zone 11b · No frost · 0 chill hours · 82 in rainZone 11b · No frost · 0 chill hours · 2,085 mm rain

    Shares 23 of your 23 great-fit crops · Also great there: cacao

    87%match
  15. 15

    🇧🇷Miracatu↻Seasons flipped. São Paulo, Brazil

    Zone 11b · No frost · 0 chill hours · 65 in rainZone 11b · No frost · 0 chill hours · 1,643 mm rain

    Shares 23 of your 23 great-fit crops

    87%match
  16. 16

    🇧🇷Florianópolis↻Seasons flipped. Santa Catarina, Brazil

    Zone 11a · No frost · 0 chill hours · 65 in rainZone 11a · No frost · 0 chill hours · 1,659 mm rain

    Shares 22 of your 23 great-fit crops

    87%match
  17. 17

    🇨🇳ShantouGuangdong, China

    Zone 10b · No frost · 10 chill hours · 58 in rainZone 10b · No frost · 10 chill hours · 1,484 mm rain

    Shares 23 of your 23 great-fit crops · Also great there: fig

    86%match
  18. 18

    🇧🇷Ubatuba↻Seasons flipped. São Paulo, Brazil

    Zone 11b · No frost · 0 chill hours · 96 in rainZone 11b · No frost · 0 chill hours · 2,444 mm rain

    Shares 23 of your 23 great-fit crops · Also great there: cacao

    86%match
  19. 19

    🇧🇷Itanhaém↻Seasons flipped. São Paulo, Brazil

    Zone 12a · No frost · 0 chill hours · 79 in rainZone 12a · No frost · 0 chill hours · 2,018 mm rain

    Shares 23 of your 23 great-fit crops · Also great there: cacao

    85%match
  20. 20

    🇨🇳PuningGuangdong, China

    Zone 10b · No frost · 13 chill hours · 58 in rainZone 10b · No frost · 13 chill hours · 1,462 mm rain

    Shares 23 of your 23 great-fit crops · Also great there: fig

    85%match

Any town over 15,000 people, skipping those within 75 km of you. ↻ = seasons flipped across the equator.

How matching works

We compare all twelve months of average highs and lows, rain, humidity and sunshine, the same way as Climatch's places-like search, but weighted for growing: winter lows count most, since frost, chill and hardiness decide which perennials survive, then summer highs and rain. Humidity counts least.

Scores show how unusual a match this close is: 80 or better is closer than all but about 2% of random pairs of places. Across the equator seasons are lined up (↻), so a southern farm's January is compared with a northern July. Climate is only part of it: check soil, water and day length (it matters for photoperiod crops) before buying land.

Looking for land?

We're building detailed reports on land like Daxi: frost risk year by year, water, and how each match's climate is shifting by 2050.

We'll only use your email for the report and related updates.

Climate data: TerraClimate 1991–2020 monthly averages (Abatzoglou, J.T. et al., 2018, Scientific Data 5:170191). Humidity is derived from TerraClimate vapor pressure; “sunshine” is solar radiation.

Places: GeoNames (CC BY 4.0). Map: © OpenStreetMap contributors, tiles by OpenFreeMap.

Grower tools: crop ranges from FAO ECOCROP and university extension guides (cited per crop). Daily weather, frost dates and chill are estimated from the monthly averages; chill portions use the Dynamic Model (Fishman, Erez & Couvillon, 1987), hardiness zones follow the USDA definition.