Climatch

Land with a climate like Pār Naogaon

🇧🇩Pār Naogaon, Rajshahi Division, Bangladesh

The towns with the closest growing climate to Pār Naogaon are Pābna, Bangladesh (99% match), Dinajpur and Bera. About 223,000 sq mi of land in 6 countries matches 80% or better, most of it in West Bengal, India, Assam, India and Sagaing Region, Myanmar.The towns with the closest growing climate to Pār Naogaon are Pābna, Bangladesh (99% match), Dinajpur and Bera. About 578,000 km² of land in 6 countries matches 80% or better, most of it in West Bengal, India, Assam, India and Sagaing Region, Myanmar.

Your growing climate

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

What grows in Pār Naogaon →

Regions with the most land like yours

  1. 1

    🇮🇳West Bengal, India

    31k sq mi82k km² matching 80% or better

    For example Gangārāmpur (99), Kāliyāganj (99)

  2. 2

    🇮🇳Assam, India

    19k sq mi49k km² matching 80% or better

    For example Nagaon (95), Morigaon (95)

  3. 3

    🇲🇲Sagaing Region, Myanmar

    18k sq mi46k km² matching 80% or better

    For example Tatkon (94), Tamu (94)

  4. 4

    🇮🇳Bihar, India

    16k sq mi42k km² matching 80% or better

    For example Kishanganj (99), Thākurganj (98)

  5. 5

    🇲🇲Kachin State, Myanmar

    13k sq mi34k km² matching 80% or better

    For example Mogaung (96), Hopin (93)

  6. 6

    🇧🇩Chittagong, Bangladesh

    11k sq mi28k km² matching 80% or better

    For example Khagrachhari (96), Shāhbāzpur Town (95)

  7. 7

    🇧🇩Khulna Division, Bangladesh

    8,800 sq mi23k km² matching 80% or better

    For example Bherāmāra (99), Kushtia (99)

  8. 8

    🇧🇩Dhaka Division, Bangladesh

    7,400 sq mi19k km² matching 80% or better

    For example Tangail (99), Sakhipur (99)

  9. 9

    🇹🇭Udon Thani, Thailand

    4,000 sq mi10k km² matching 80% or better

    For example Ban Dung (88), Nam Som (82)

  10. 10

    🇳🇵Madhesh, Nepal

    3,700 sq mi9,700 km² matching 80% or better

    For example Rājbirāj (93), Lahān (92)

  11. 11

    🇱🇦Savannakhet Province, Laos

    3,100 sq mi8,200 km² matching 80% or better

    For example Ban Xénô (82), Savannakhet (80)

  12. 12

    🇹🇭Sakon Nakhon, Thailand

    2,900 sq mi7,400 km² matching 80% or better

    For example Sawang Daen Din (86), Warichaphum (83)

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

    🇧🇩PābnaRajshahi Division, Bangladesh

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

    Shares 18 of your 19 great-fit crops

    99%match
  2. 2

    🇧🇩DinajpurRangpur Division, Bangladesh

    Zone 10b · No frost · 8 chill hours · 77 in rainZone 10b · No frost · 8 chill hours · 1,955 mm rain

    Shares 17 of your 19 great-fit crops

    99%match
  3. 3

    🇧🇩BeraRajshahi Division, Bangladesh

    Zone 11a · No frost · 0 chill hours · 68 in rainZone 11a · No frost · 0 chill hours · 1,732 mm rain

    Shares 18 of your 19 great-fit crops

    99%match
  4. 4

    🇧🇩JamālpurMymensingh Division, Bangladesh

    Zone 11a · No frost · 0 chill hours · 84 in rainZone 11a · No frost · 0 chill hours · 2,136 mm rain

    Shares 18 of your 19 great-fit crops

    99%match
  5. 5

    🇮🇳RāiganjWest Bengal, India

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

    Shares 16 of your 19 great-fit crops

    99%match
  6. 6

    🇧🇩ThākurgaonRangpur Division, Bangladesh

    Zone 10b · No frost · 4 chill hours · 90 in rainZone 10b · No frost · 4 chill hours · 2,282 mm rain

    Shares 17 of your 19 great-fit crops

    99%match
  7. 7

    🇧🇩MymensinghMymensingh Division, Bangladesh

    Zone 11a · No frost · 0 chill hours · 89 in rainZone 11a · No frost · 0 chill hours · 2,252 mm rain

    Shares 18 of your 19 great-fit crops · Also great there: macadamia

    99%match
  8. 8

    🇮🇳MaldaWest Bengal, India

    Zone 10b · No frost · 3 chill hours · 56 in rainZone 10b · No frost · 3 chill hours · 1,411 mm rain

    Shares 15 of your 19 great-fit crops · Also great there: fig

    99%match
  9. 9

    🇮🇳KishanganjBihar, India

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

    Shares 16 of your 19 great-fit crops

    99%match
  10. 10

    🇧🇩NāgarpurDhaka Division, Bangladesh

    Zone 11a · No frost · 2 chill hours · 73 in rainZone 11a · No frost · 2 chill hours · 1,855 mm rain

    Shares 18 of your 19 great-fit crops

    99%match
  11. 11

    🇮🇳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 19 great-fit crops

    98%match
  12. 12

    🇧🇩RangpurRangpur Division, Bangladesh

    Zone 11a · No frost · 1 chill hours · 97 in rainZone 11a · No frost · 1 chill hours · 2,452 mm rain

    Shares 18 of your 19 great-fit crops · Also great there: macadamia

    98%match
  13. 13

    🇮🇳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 19 great-fit crops

    98%match
  14. 14

    🇮🇳JangipurWest Bengal, India

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

    Shares 17 of your 19 great-fit crops · Also great there: fig

    98%match
  15. 15

    🇮🇳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 19 great-fit crops

    97%match
  16. 16

    🇮🇳BaharampurWest Bengal, India

    Zone 11a · No frost · 0 chill hours · 59 in rainZone 11a · No frost · 0 chill hours · 1,486 mm rain

    Shares 17 of your 19 great-fit crops · Also great there: fig

    97%match
  17. 17

    🇳🇵BiratnagarKoshi, Nepal

    Zone 10b · No frost · 11 chill hours · 79 in rainZone 10b · No frost · 11 chill hours · 1,998 mm rain

    Shares 15 of your 19 great-fit crops

    97%match
  18. 18

    🇧🇩ChilmāriRangpur Division, Bangladesh

    Zone 11a · No frost · 0 chill hours · 98 in rainZone 11a · No frost · 0 chill hours · 2,491 mm rain

    Shares 18 of your 19 great-fit crops · Also great there: macadamia

    97%match
  19. 19

    🇧🇩KhagrachhariChittagong, Bangladesh

    Zone 11a · No frost · 0 chill hours · 89 in rainZone 11a · No frost · 0 chill hours · 2,250 mm rain

    Shares 18 of your 19 great-fit crops

    96%match
  20. 20

    🇮🇳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 19 great-fit crops

    96%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 Pār Naogaon: 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.