Climatch

Land with a climate like Parauapebas

🇧🇷Parauapebas, Pará, Brazil

The towns with the closest growing climate to Parauapebas are Xinguara, Brazil (99% match), São Geraldo do Araguaia and São Domingos do Araguaia. About 1.7 million sq mi of land in 23 countries matches 80% or better, most of it in Pará, Brazil, Mato Grosso, Brazil and Rondônia, Brazil.The towns with the closest growing climate to Parauapebas are Xinguara, Brazil (99% match), São Geraldo do Araguaia and São Domingos do Araguaia. About 4.4 million km² of land in 23 countries matches 80% or better, most of it in Pará, Brazil, Mato Grosso, Brazil and Rondônia, Brazil.

Your growing climate

Zone 13a · No frost · 0 chill hours · 66 in rainZone 13a · No frost · 0 chill hours · 1,672 mm rain

What grows in Parauapebas →

Regions with the most land like yours

  1. 1

    🇧🇷Pará, Brazil

    154k sq mi400k km² matching 80% or better

    For example Xinguara (99), São Geraldo do Araguaia (99)

  2. 2

    🇧🇷Mato Grosso, Brazil

    85k sq mi219k km² matching 80% or better

    For example Colniza (93), Aripuanã (92)

  3. 3

    🇧🇷Rondônia, Brazil

    81k sq mi210k km² matching 80% or better

    For example São Francisco do Guaporé (95), Nova Mamoré (94)

  4. 4

    🇨🇩Kwilu, Democratic Republic of the Congo

    23k sq mi59k km² matching 80% or better

    For example Masi-Manimba (86), Bulungu (84)

  5. 5

    🇨🇲East, Cameroon

    21k sq mi53k km² matching 80% or better

    For example Batouri (90), Yokadouma (89)

  6. 6

    🇧🇴Beni Department, Bolivia

    19k sq mi50k km² matching 80% or better

    For example Guayaramerín (93), Riberalta (92)

  7. 7

    🇨🇩Mongala, Democratic Republic of the Congo

    18k sq mi46k km² matching 80% or better

    For example Lisala (86), Binga (85)

  8. 8

    🇨🇩Bas-Uele, Democratic Republic of the Congo

    18k sq mi46k km² matching 80% or better

    For example Aketi (86), Bondo (85)

  9. 9

    🇨🇲Centre, Cameroon

    17k sq mi44k km² matching 80% or better

    For example Bafia (89), Nanga Eboko (88)

  10. 10

    🇨🇬Likouala, Republic of the Congo

    12k sq mi31k km² matching 80% or better

    For example Bétou (88), Mindouli (88)

  11. 11

    🇨🇴Vichada Department, Colombia

    11k sq mi29k km² matching 80% or better

    For example Cumaribo (83), Puerto Carreño (82)

  12. 12

    🇮🇩East Java, Indonesia

    10k sq mi26k km² matching 80% or better

    For example Bondowoso (87), Blitar (85)

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

    🇧🇷XinguaraPará, Brazil

    Zone 12b · No frost · 0 chill hours · 66 in rainZone 12b · No frost · 0 chill hours · 1,664 mm rain

    Shares 17 of your 17 great-fit crops

    99%match
  2. 2

    🇧🇷São Geraldo do AraguaiaPará, Brazil

    Zone 12b · No frost · 0 chill hours · 54 in rainZone 12b · No frost · 0 chill hours · 1,364 mm rain

    Shares 16 of your 17 great-fit crops · Also great there: fig

    99%match
  3. 3

    🇧🇷São Domingos do AraguaiaPará, Brazil

    Zone 13a · No frost · 0 chill hours · 60 in rainZone 13a · No frost · 0 chill hours · 1,536 mm rain

    Shares 15 of your 17 great-fit crops

    99%match
  4. 4

    🇧🇷MarabáPará, Brazil

    Zone 13a · No frost · 0 chill hours · 64 in rainZone 13a · No frost · 0 chill hours · 1,622 mm rain

    Shares 15 of your 17 great-fit crops

    99%match
  5. 5

    🇧🇷Água Azul do NortePará, Brazil

    Zone 12b · No frost · 0 chill hours · 68 in rainZone 12b · No frost · 0 chill hours · 1,730 mm rain

    Shares 17 of your 17 great-fit crops

    99%match
  6. 6

    🇧🇷Bom Jesus do TocantinsPará, Brazil

    Zone 13a · No frost · 0 chill hours · 63 in rainZone 13a · No frost · 0 chill hours · 1,590 mm rain

    Shares 13 of your 17 great-fit crops

    99%match
  7. 7

    🇧🇷Nova IpixunaPará, Brazil

    Zone 13a · No frost · 0 chill hours · 67 in rainZone 13a · No frost · 0 chill hours · 1,703 mm rain

    Shares 15 of your 17 great-fit crops

    99%match
  8. 8

    🇧🇷Floresta do AraguaiaPará, Brazil

    Zone 12b · No frost · 0 chill hours · 65 in rainZone 12b · No frost · 0 chill hours · 1,648 mm rain

    Shares 16 of your 17 great-fit crops

    99%match
  9. 9

    🇧🇷Rondon do ParáPará, Brazil

    Zone 13a · No frost · 0 chill hours · 61 in rainZone 13a · No frost · 0 chill hours · 1,542 mm rain

    Shares 15 of your 17 great-fit crops

    98%match
  10. 10

    🇧🇷Novo ProgressoPará, Brazil

    Zone 13a · No frost · 0 chill hours · 85 in rainZone 13a · No frost · 0 chill hours · 2,167 mm rain

    Shares 15 of your 17 great-fit crops

    98%match
  11. 11

    🇧🇷AraguaínaTocantins, Brazil

    Zone 12b · No frost · 0 chill hours · 62 in rainZone 12b · No frost · 0 chill hours · 1,570 mm rain

    Shares 17 of your 17 great-fit crops

    98%match
  12. 12

    🇧🇷Dom EliseuPará, Brazil

    Zone 13a · No frost · 0 chill hours · 61 in rainZone 13a · No frost · 0 chill hours · 1,562 mm rain

    Shares 15 of your 17 great-fit crops

    98%match
  13. 13

    🇧🇷AraguatinsTocantins, Brazil

    Zone 13a · No frost · 0 chill hours · 56 in rainZone 13a · No frost · 0 chill hours · 1,424 mm rain

    Shares 15 of your 17 great-fit crops · Also great there: fig

    98%match
  14. 14

    🇧🇷RedençãoPará, Brazil

    Zone 12b · No frost · 0 chill hours · 67 in rainZone 12b · No frost · 0 chill hours · 1,710 mm rain

    Shares 16 of your 17 great-fit crops

    97%match
  15. 15

    🇧🇷São Félix do XinguPará, Brazil

    Zone 12b · No frost · 0 chill hours · 73 in rainZone 12b · No frost · 0 chill hours · 1,847 mm rain

    Shares 17 of your 17 great-fit crops

    97%match
  16. 16

    🇧🇷JacundáPará, Brazil

    Zone 13a · No frost · 0 chill hours · 75 in rainZone 13a · No frost · 0 chill hours · 1,900 mm rain

    Shares 13 of your 17 great-fit crops

    97%match
  17. 17

    🇧🇷TucumãPará, Brazil

    Zone 12b · No frost · 0 chill hours · 70 in rainZone 12b · No frost · 0 chill hours · 1,770 mm rain

    Shares 17 of your 17 great-fit crops

    96%match
  18. 18

    🇧🇷ApuíAmazonas, Brazil

    Zone 13a · No frost · 0 chill hours · 76 in rainZone 13a · No frost · 0 chill hours · 1,943 mm rain

    Shares 15 of your 17 great-fit crops

    96%match
  19. 19

    🇧🇷Novo RepartimentoPará, Brazil

    Zone 13a · No frost · 0 chill hours · 76 in rainZone 13a · No frost · 0 chill hours · 1,939 mm rain

    Shares 14 of your 17 great-fit crops

    96%match
  20. 20

    🇧🇷UlianópolisPará, Brazil

    Zone 13a · No frost · 0 chill hours · 68 in rainZone 13a · No frost · 0 chill hours · 1,737 mm rain

    Shares 14 of your 17 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 Parauapebas: 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.