Climatch

Land with a climate like Harūr

🇮🇳Harūr, Tamil Nadu, India

The towns with the closest growing climate to Harūr are Vellore, India (99% match), Ambur and Polūr. About 115,000 sq mi of land in 8 countries matches 80% or better, most of it in Tamil Nadu, India, Andhra Pradesh, India and Maranhão, Brazil.The towns with the closest growing climate to Harūr are Vellore, India (99% match), Ambur and Polūr. About 298,000 km² of land in 8 countries matches 80% or better, most of it in Tamil Nadu, India, Andhra Pradesh, India and Maranhão, Brazil.

Your growing climate

Zone 12b · No frost · 0 chill hours · 40 in rainZone 12b · No frost · 0 chill hours · 1,022 mm rain

What grows in Harūr →

Regions with the most land like yours

  1. 1

    🇮🇳Tamil Nadu, India

    40k sq mi103k km² matching 80% or better

    For example Pallikonda (99), Gudiyatham (99)

  2. 2

    🇮🇳Andhra Pradesh, India

    24k sq mi63k km² matching 80% or better

    For example Rāmāpuram (99), Chittoor (99)

  3. 3

    🇧🇷Maranhão, Brazil

    18k sq mi46k km² matching 80% or better

    For example Santa Quitéria do Maranhão (85), Coelho Neto (84)

  4. 4

    🇧🇷Piauí, Brazil

    14k sq mi36k km² matching 80% or better

    For example Batalha (87), José de Freitas (86)

  5. 5

    🇨🇴La Guajira Department, Colombia

    2,600 sq mi6,800 km² matching 80% or better

    For example Fonseca (86), Hatonuevo (86)

  6. 6

    🇨🇴Cesar Department, Colombia

    1,800 sq mi4,600 km² matching 80% or better

    For example Valledupar (85), San Diego (82)

  7. 7

    🇨🇴Magdalena Department, Colombia

    1,800 sq mi4,600 km² matching 80% or better

    For example Fundación (81), Aracataca (80)

  8. 8

    🇻🇪Falcón, Venezuela

    1,500 sq mi3,800 km² matching 80% or better

    For example Mene de Mauroa (82), Dabajuro (81)

  9. 9

    🇻🇪Anzoátegui, Venezuela

    1,200 sq mi3,100 km² matching 80% or better

    For example Aragua de Barcelona (79), Clarines (78)

  10. 10

    🇰🇭Kampong Speu Province, Cambodia

    1,200 sq mi3,000 km² matching 80% or better

    For example Phumĭ Véal Tônsay (82), Kampong Speu (80)

  11. 11

    🇮🇳Karnataka, India

    1,200 sq mi3,000 km² matching 80% or better

    For example Kanakapura (80), Bāgepalli (77)

  12. 12

    🇰🇭Prey Veng, Cambodia

    900 sq mi2,300 km² matching 80% or better

    For example Prey Veng (80)

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

    🇮🇳VelloreTamil Nadu, India

    Zone 12b · No frost · 0 chill hours · 42 in rainZone 12b · No frost · 0 chill hours · 1,070 mm rain

    Shares 15 of your 15 great-fit crops

    99%match
  2. 2

    🇮🇳AmburTamil Nadu, India

    Zone 12a · No frost · 0 chill hours · 37 in rainZone 12a · No frost · 0 chill hours · 943 mm rain

    Shares 15 of your 15 great-fit crops

    99%match
  3. 3

    🇮🇳PolūrTamil Nadu, India

    Zone 12b · No frost · 0 chill hours · 46 in rainZone 12b · No frost · 0 chill hours · 1,158 mm rain

    Shares 15 of your 15 great-fit crops

    99%match
  4. 4

    🇮🇳TiruchengodeTamil Nadu, India

    Zone 12b · No frost · 0 chill hours · 35 in rainZone 12b · No frost · 0 chill hours · 899 mm rain

    Shares 15 of your 15 great-fit crops

    98%match
  5. 5

    🇮🇳TirukkoyilurTamil Nadu, India

    Zone 12b · No frost · 0 chill hours · 49 in rainZone 12b · No frost · 0 chill hours · 1,241 mm rain

    Shares 15 of your 15 great-fit crops · Also great there: banana

    98%match
  6. 6

    🇮🇳PerambalurTamil Nadu, India

    Zone 12b · No frost · 0 chill hours · 43 in rainZone 12b · No frost · 0 chill hours · 1,087 mm rain

    Shares 15 of your 15 great-fit crops

    97%match
  7. 7

    🇮🇳TirupatiAndhra Pradesh, India

    Zone 12b · No frost · 0 chill hours · 40 in rainZone 12b · No frost · 0 chill hours · 1,012 mm rain

    Shares 15 of your 15 great-fit crops

    97%match
  8. 8

    🇮🇳ErodeTamil Nadu, India

    Zone 13a · No frost · 0 chill hours · 33 in rainZone 13a · No frost · 0 chill hours · 844 mm rain

    Shares 15 of your 15 great-fit crops

    97%match
  9. 9

    🇮🇳DindigulTamil Nadu, India

    Zone 13a · No frost · 0 chill hours · 37 in rainZone 13a · No frost · 0 chill hours · 941 mm rain

    Shares 15 of your 15 great-fit crops

    97%match
  10. 10

    🇮🇳CheyyarTamil Nadu, India

    Zone 12b · No frost · 0 chill hours · 48 in rainZone 12b · No frost · 0 chill hours · 1,220 mm rain

    Shares 15 of your 15 great-fit crops

    97%match
  11. 11

    🇮🇳PīleruAndhra Pradesh, India

    Zone 12a · No frost · 0 chill hours · 30 in rainZone 12a · No frost · 0 chill hours · 768 mm rain

    Shares 15 of your 15 great-fit crops · Also great there: wheat

    96%match
  12. 12

    🇮🇳UdumalaippettaiTamil Nadu, India

    Zone 12b · No frost · 0 chill hours · 33 in rainZone 12b · No frost · 0 chill hours · 837 mm rain

    Shares 15 of your 15 great-fit crops · Also great there: table and raisin grapes

    96%match
  13. 13

    🇮🇳KanchipuramTamil Nadu, India

    Zone 12b · No frost · 0 chill hours · 48 in rainZone 12b · No frost · 0 chill hours · 1,225 mm rain

    Shares 15 of your 15 great-fit crops · Also great there: banana

    96%match
  14. 14

    🇮🇳SathyamangalamTamil Nadu, India

    Zone 12b · No frost · 0 chill hours · 32 in rainZone 12b · No frost · 0 chill hours · 820 mm rain

    Shares 15 of your 15 great-fit crops

    95%match
  15. 15

    🇮🇳MannachanallurTamil Nadu, India

    Zone 13a · No frost · 0 chill hours · 36 in rainZone 13a · No frost · 0 chill hours · 909 mm rain

    Shares 15 of your 15 great-fit crops

    95%match
  16. 16

    🇮🇳TiruchirappalliTamil Nadu, India

    Zone 13a · No frost · 0 chill hours · 36 in rainZone 13a · No frost · 0 chill hours · 920 mm rain

    Shares 15 of your 15 great-fit crops

    94%match
  17. 17

    🇮🇳KarurTamil Nadu, India

    Zone 13a · No frost · 0 chill hours · 31 in rainZone 13a · No frost · 0 chill hours · 779 mm rain

    Shares 15 of your 15 great-fit crops

    94%match
  18. 18

    🇮🇳CoimbatoreTamil Nadu, India

    Zone 12b · No frost · 0 chill hours · 39 in rainZone 12b · No frost · 0 chill hours · 995 mm rain

    Shares 15 of your 15 great-fit crops · Also great there: avocado

    94%match
  19. 19

    🇮🇳Kanigiri UAndhra Pradesh, India

    Zone 12b · No frost · 0 chill hours · 32 in rainZone 12b · No frost · 0 chill hours · 820 mm rain

    Shares 15 of your 15 great-fit crops

    92%match
  20. 20

    🇮🇳PalmanerAndhra Pradesh, India

    Zone 12a · No frost · 0 chill hours · 33 in rainZone 12a · No frost · 0 chill hours · 836 mm rain

    Shares 15 of your 15 great-fit crops · Also great there: wheat, table and raisin grapes, canola (rapeseed)

    92%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 Harūr: 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.