Climatch

Land with a climate like Jangipur

🇮🇳Jangipur, West Bengal, India

The towns with the closest growing climate to Jangipur are Shāntipur, India (99% match), Arāmbāgh and Barddhamān. About 174,000 sq mi of land in 6 countries matches 80% or better, most of it in West Bengal, India, Bihar, India and Odisha, India.The towns with the closest growing climate to Jangipur are Shāntipur, India (99% match), Arāmbāgh and Barddhamān. About 451,000 km² of land in 6 countries matches 80% or better, most of it in West Bengal, India, Bihar, India and Odisha, India.

Your growing climate

Zone 10b · No frost · 7 chill hours · 58 in rainZone 10b · No frost · 7 chill hours · 1,461 mm rain

What grows in Jangipur →

Regions with the most land like yours

  1. 1

    🇮🇳West Bengal, India

    32k sq mi84k km² matching 80% or better

    For example Kātoya (99), Navadwīp (99)

  2. 2

    🇮🇳Bihar, India

    28k sq mi72k km² matching 80% or better

    For example Purnia (99), Kasba (99)

  3. 3

    🇮🇳Odisha, India

    28k sq mi72k km² matching 80% or better

    For example Jaleshwar (92), Baripāda (92)

  4. 4

    🇲🇲Sagaing Region, Myanmar

    12k sq mi30k km² matching 80% or better

    For example Mawlaik (85), Tatkon (85)

  5. 5

    🇧🇩Rajshahi Division, Bangladesh

    7,300 sq mi19k km² matching 80% or better

    For example Puthia (99), Natore (98)

  6. 6

    🇲🇲Kachin State, Myanmar

    6,000 sq mi15k km² matching 80% or better

    For example Mogaung (88), Myitkyina (87)

  7. 7

    🇧🇩Rangpur Division, Bangladesh

    4,800 sq mi13k km² matching 80% or better

    For example Pīrgaaj (95), Dinajpur (94)

  8. 8

    🇧🇩Chittagong, Bangladesh

    4,700 sq mi12k km² matching 80% or better

    For example Khagrachhari (88), Manikchari (86)

  9. 9

    🇳🇵Madhesh, Nepal

    3,500 sq mi9,000 km² matching 80% or better

    For example Rājbirāj (93), Birgañj (91)

  10. 10

    🇹🇭Udon Thani, Thailand

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

    For example Ban Dung (81), Ban Nong Wua So (80)

  11. 11

    🇳🇵Koshi, Nepal

    1,600 sq mi4,200 km² matching 80% or better

    For example Inaruwa (95), Biratnagar (95)

  12. 12

    🇹🇭Nong Khai, Thailand

    1,400 sq mi3,700 km² matching 80% or better

    For example Nong Khai (82), Phon Charoen (82)

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

    🇮🇳ShāntipurWest Bengal, India

    Zone 11a · No frost · 1 chill hours · 60 in rainZone 11a · No frost · 1 chill hours · 1,514 mm rain

    Shares 17 of your 18 great-fit crops

    99%match
  2. 2

    🇮🇳ArāmbāghWest Bengal, India

    Zone 11a · No frost · 0 chill hours · 51 in rainZone 11a · No frost · 0 chill hours · 1,306 mm rain

    Shares 16 of your 18 great-fit crops

    99%match
  3. 3

    🇮🇳BarddhamānWest Bengal, India

    Zone 11a · No frost · 1 chill hours · 57 in rainZone 11a · No frost · 1 chill hours · 1,438 mm rain

    Shares 17 of your 18 great-fit crops

    99%match
  4. 4

    🇮🇳DumkaJharkhand, India

    Zone 10b · No frost · 8 chill hours · 55 in rainZone 10b · No frost · 8 chill hours · 1,387 mm rain

    Shares 16 of your 18 great-fit crops

    99%match
  5. 5

    🇮🇳GoddaJharkhand, India

    Zone 10b · Frost 1 years in 10 · 16 chill hours · 51 in rainZone 10b · Frost 1 years in 10 · 16 chill hours · 1,297 mm rain

    Shares 17 of your 18 great-fit crops

    99%match
  6. 6

    🇮🇳PurniaBihar, India

    Zone 10b · No frost · 15 chill hours · 61 in rainZone 10b · No frost · 15 chill hours · 1,539 mm rain

    Shares 15 of your 18 great-fit crops

    99%match
  7. 7

    🇮🇳BolpurWest Bengal, India

    Zone 11a · No frost · 2 chill hours · 56 in rainZone 11a · No frost · 2 chill hours · 1,425 mm rain

    Shares 17 of your 18 great-fit crops

    99%match
  8. 8

    🇮🇳RāiganjWest Bengal, India

    Zone 10b · No frost · 12 chill hours · 66 in rainZone 10b · No frost · 12 chill hours · 1,680 mm rain

    Shares 15 of your 18 great-fit crops · Also great there: avocado

    98%match
  9. 9

    🇧🇩NatoreRajshahi Division, Bangladesh

    Zone 10b · No frost · 3 chill hours · 67 in rainZone 10b · No frost · 3 chill hours · 1,700 mm rain

    Shares 16 of your 18 great-fit crops · Also great there: canola (rapeseed), avocado

    98%match
  10. 10

    🇮🇳KatiharBihar, India

    Zone 10b · No frost · 16 chill hours · 57 in rainZone 10b · No frost · 16 chill hours · 1,442 mm rain

    Shares 16 of your 18 great-fit crops

    98%match
  11. 11

    🇮🇳BhātpāraWest Bengal, India

    Zone 11a · No frost · 0 chill hours · 65 in rainZone 11a · No frost · 0 chill hours · 1,656 mm rain

    Shares 17 of your 18 great-fit crops

    98%match
  12. 12

    🇮🇳BālurghātWest Bengal, India

    Zone 10b · No frost · 4 chill hours · 72 in rainZone 10b · No frost · 4 chill hours · 1,829 mm rain

    Shares 16 of your 18 great-fit crops · Also great there: avocado

    98%match
  13. 13

    🇧🇩Pār NaogaonRajshahi Division, Bangladesh

    Zone 11a · No frost · 0 chill hours · 66 in rainZone 11a · No frost · 0 chill hours · 1,676 mm rain

    Shares 17 of your 18 great-fit crops · Also great there: avocado, canola (rapeseed)

    98%match
  14. 14

    🇮🇳ArāriaBihar, India

    Zone 10b · No frost · 11 chill hours · 77 in rainZone 10b · No frost · 11 chill hours · 1,954 mm rain

    Shares 15 of your 18 great-fit crops

    97%match
  15. 15

    🇮🇳BhāgalpurBihar, India

    Zone 10b · No frost · 15 chill hours · 46 in rainZone 10b · No frost · 15 chill hours · 1,177 mm rain

    Shares 16 of your 18 great-fit crops

    97%match
  16. 16

    🇮🇳KishanganjBihar, India

    Zone 10b · No frost · 10 chill hours · 83 in rainZone 10b · No frost · 10 chill hours · 2,103 mm rain

    Shares 15 of your 18 great-fit crops · Also great there: avocado

    96%match
  17. 17

    🇧🇩PābnaRajshahi Division, Bangladesh

    Zone 10b · No frost · 2 chill hours · 67 in rainZone 10b · No frost · 2 chill hours · 1,700 mm rain

    Shares 16 of your 18 great-fit crops · Also great there: canola (rapeseed), avocado

    95%match
  18. 18

    🇮🇳DurgapurWest Bengal, India

    Zone 11a · No frost · 1 chill hours · 53 in rainZone 11a · No frost · 1 chill hours · 1,336 mm rain

    Shares 16 of your 18 great-fit crops

    95%match
  19. 19

    🇮🇳SaharsaBihar, India

    Zone 10b · Frost 1 years in 10 · 23 chill hours · 48 in rainZone 10b · Frost 1 years in 10 · 23 chill hours · 1,218 mm rain

    Shares 15 of your 18 great-fit crops

    95%match
  20. 20

    🇮🇳DeogharJharkhand, India

    Zone 10b · Frost 1 years in 10 · 14 chill hours · 50 in rainZone 10b · Frost 1 years in 10 · 14 chill hours · 1,272 mm rain

    Shares 16 of your 18 great-fit crops

    95%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 Jangipur: 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.