Climatch

Land with a climate like Gundlupēt

🇮🇳Gundlupēt, Karnataka, India

The towns with the closest growing climate to Gundlupēt are Krishnarājāsāgara, India (99% match), Mandya and Bengaluru. About 188,000 sq mi of land in 19 countries matches 80% or better, most of it in Karnataka, India, Maranhão, Brazil and Northern Region, Uganda.The towns with the closest growing climate to Gundlupēt are Krishnarājāsāgara, India (99% match), Mandya and Bengaluru. About 487,000 km² of land in 19 countries matches 80% or better, most of it in Karnataka, India, Maranhão, Brazil and Northern Region, Uganda.

Your growing climate

Zone 12a · No frost · 0 chill hours · 36 in rainZone 12a · No frost · 0 chill hours · 904 mm rain

What grows in Gundlupēt →

Regions with the most land like yours

  1. 1

    🇮🇳Karnataka, India

    33k sq mi86k km² matching 80% or better

    For example Krishnarājāsāgara (99), French Rocks (99)

  2. 2

    🇧🇷Maranhão, Brazil

    21k sq mi55k km² matching 80% or better

    For example São João dos Patos (85), Paraibano (84)

  3. 3

    🇺🇬Northern Region, Uganda

    10k sq mi27k km² matching 80% or better

    For example Anaka (82), Kitgum (81)

  4. 4

    🇮🇳Tamil Nadu, India

    9,100 sq mi23k km² matching 80% or better

    For example Mūkondapalli (99), Jūjūvādi (99)

  5. 5

    🇨🇫Ombella-M'Poko, Central African Republic

    5,700 sq mi15k km² matching 80% or better

    For example Bégoua (80), Bimbo (80)

  6. 6

    🇹🇭Lampang, Thailand

    4,600 sq mi12k km² matching 80% or better

    For example Lampang (77), Thoen (77)

  7. 7

    🇸🇸Western Equatoria, South Sudan

    4,500 sq mi12k km² matching 80% or better

    For example Yambio (80)

  8. 8

    🇨🇫Kémo, Central African Republic

    4,500 sq mi12k km² matching 80% or better

    For example Sibut (80), Dekoa (76)

  9. 9

    🇹🇭Chiang Mai, Thailand

    4,200 sq mi11k km² matching 80% or better

    For example San Pa Tong (79), Hang Dong (78)

  10. 10

    🇨🇫Mbomou, Central African Republic

    4,200 sq mi11k km² matching 80% or better

    For example Bangassou (82)

  11. 11

    🇹🇿Mwanza, Tanzania

    3,900 sq mi10k km² matching 80% or better

    For example Magu (87), Kihangara (87)

  12. 12

    🇹🇿Kigoma, Tanzania

    3,600 sq mi9,300 km² matching 80% or better

    For example Mwandiga (85), Kigoma (84)

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

    🇮🇳KrishnarājāsāgaraKarnataka, India

    Zone 12a · No frost · 0 chill hours · 30 in rainZone 12a · No frost · 0 chill hours · 766 mm rain

    Shares 18 of your 20 great-fit crops · Also great there: barley

    99%match
  2. 2

    🇮🇳MandyaKarnataka, India

    Zone 12a · No frost · 0 chill hours · 31 in rainZone 12a · No frost · 0 chill hours · 776 mm rain

    Shares 19 of your 20 great-fit crops

    98%match
  3. 3

    🇮🇳BengaluruKarnataka, India

    Zone 12a · No frost · 0 chill hours · 36 in rainZone 12a · No frost · 0 chill hours · 914 mm rain

    Shares 19 of your 20 great-fit crops · Also great there: barley

    98%match
  4. 4

    🇮🇳HassanKarnataka, India

    Zone 12a · No frost · 0 chill hours · 35 in rainZone 12a · No frost · 0 chill hours · 891 mm rain

    Shares 20 of your 20 great-fit crops · Also great there: barley, potato

    97%match
  5. 5

    🇮🇳RobertsonpetKarnataka, India

    Zone 12a · No frost · 0 chill hours · 30 in rainZone 12a · No frost · 0 chill hours · 750 mm rain

    Shares 19 of your 20 great-fit crops · Also great there: barley

    96%match
  6. 6

    🇮🇳ClosepetKarnataka, India

    Zone 12a · No frost · 0 chill hours · 32 in rainZone 12a · No frost · 0 chill hours · 812 mm rain

    Shares 19 of your 20 great-fit crops

    96%match
  7. 7

    🇮🇳TiptūrKarnataka, India

    Zone 12a · No frost · 0 chill hours · 24 in rainZone 12a · No frost · 0 chill hours · 615 mm rain

    Shares 16 of your 20 great-fit crops · Also great there: barley

    94%match
  8. 8

    🇮🇳ChintamaniKarnataka, India

    Zone 11b · No frost · 0 chill hours · 28 in rainZone 11b · No frost · 0 chill hours · 711 mm rain

    Shares 18 of your 20 great-fit crops · Also great there: barley

    93%match
  9. 9

    🇮🇳BhadrāvatiKarnataka, India

    Zone 12a · No frost · 0 chill hours · 38 in rainZone 12a · No frost · 0 chill hours · 969 mm rain

    Shares 18 of your 20 great-fit crops

    93%match
  10. 10

    🇮🇳TumkūrKarnataka, India

    Zone 12a · No frost · 0 chill hours · 31 in rainZone 12a · No frost · 0 chill hours · 786 mm rain

    Shares 19 of your 20 great-fit crops · Also great there: barley

    93%match
  11. 11

    🇮🇳GauribidanurKarnataka, India

    Zone 12a · No frost · 0 chill hours · 30 in rainZone 12a · No frost · 0 chill hours · 757 mm rain

    Shares 17 of your 20 great-fit crops

    92%match
  12. 12

    🇮🇳KushālnagarKarnataka, India

    Zone 12a · No frost · 0 chill hours · 60 in rainZone 12a · No frost · 0 chill hours · 1,532 mm rain

    Shares 18 of your 20 great-fit crops · Also great there: cacao, coffee (arabica), banana

    90%match
  13. 13

    🇮🇳HosadurgaKarnataka, India

    Zone 12a · No frost · 0 chill hours · 23 in rainZone 12a · No frost · 0 chill hours · 574 mm rain

    Shares 14 of your 20 great-fit crops

    90%match
  14. 14

    🇮🇳HubballiKarnataka, India

    Zone 12a · No frost · 0 chill hours · 35 in rainZone 12a · No frost · 0 chill hours · 895 mm rain

    Shares 18 of your 20 great-fit crops

    90%match
  15. 15

    🇮🇳MadanapalleAndhra Pradesh, India

    Zone 12a · No frost · 0 chill hours · 27 in rainZone 12a · No frost · 0 chill hours · 692 mm rain

    Shares 17 of your 20 great-fit crops

    88%match
  16. 16

    🇹🇿Magu↻Seasons flipped. Mwanza, Tanzania

    Zone 12a · No frost · 0 chill hours · 30 in rainZone 12a · No frost · 0 chill hours · 750 mm rain

    Shares 18 of your 20 great-fit crops

    87%match
  17. 17

    🇮🇳CoimbatoreTamil Nadu, India

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

    Shares 16 of your 20 great-fit crops

    87%match
  18. 18

    🇧🇮Rumonge↻Seasons flipped. Burundi

    Zone 12b · No frost · 0 chill hours · 38 in rainZone 12b · No frost · 0 chill hours · 977 mm rain

    Shares 19 of your 20 great-fit crops

    86%match
  19. 19

    🇮🇳Gadag-BetageriKarnataka, India

    Zone 12a · No frost · 0 chill hours · 31 in rainZone 12a · No frost · 0 chill hours · 781 mm rain

    Shares 17 of your 20 great-fit crops

    86%match
  20. 20

    🇮🇳SaundattiKarnataka, India

    Zone 12a · No frost · 0 chill hours · 41 in rainZone 12a · No frost · 0 chill hours · 1,039 mm rain

    Shares 17 of your 20 great-fit crops

    86%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 Gundlupēt: 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.