Climatch

Land with a climate like General Emilio Aguinaldo

🇵🇭General Emilio Aguinaldo, Calabarzon, Philippines

The towns with the closest growing climate to General Emilio Aguinaldo are Mamburao, Philippines (98% match), Sablayan and El Nido. About 517,000 sq mi of land in 19 countries matches 80% or better, most of it in Pará, Brazil, Amazonas, Brazil and Mimaropa, Philippines.The towns with the closest growing climate to General Emilio Aguinaldo are Mamburao, Philippines (98% match), Sablayan and El Nido. About 1.3 million km² of land in 19 countries matches 80% or better, most of it in Pará, Brazil, Amazonas, Brazil and Mimaropa, Philippines.

Your growing climate

Zone 13a · No frost · 0 chill hours · 91 in rainZone 13a · No frost · 0 chill hours · 2,314 mm rain

What grows in General Emilio Aguinaldo →

Regions with the most land like yours

  1. 1

    🇧🇷Pará, Brazil

    119k sq mi307k km² matching 80% or better

    For example Juruti (89), Óbidos (89)

  2. 2

    🇧🇷Amazonas, Brazil

    49k sq mi126k km² matching 80% or better

    For example Nhamundá (87), Manacapuru (86)

  3. 3

    🇵🇭Mimaropa, Philippines

    26k sq mi67k km² matching 80% or better

    For example Mamburao (98), Sablayan (98)

  4. 4

    🇮🇩East Java, Indonesia

    18k sq mi46k km² matching 80% or better

    For example Sumenep (88), Sangkapura (88)

  5. 5

    🇻🇪Bolívar, Venezuela

    13k sq mi33k km² matching 80% or better

    For example La Paragua (86), Guasipati (82)

  6. 6

    🇮🇩Central Java, Indonesia

    12k sq mi32k km² matching 80% or better

    For example Tegal (90), Bulakamba (89)

  7. 7

    🇵🇭Calabarzon, Philippines

    12k sq mi31k km² matching 80% or better

    For example Rodriguez (98), Pangil (93)

  8. 8

    🇮🇩West Java, Indonesia

    8,600 sq mi22k km² matching 80% or better

    For example Sumber (91), Karangampel (90)

  9. 9

    🇵🇭Zamboanga Peninsula, Philippines

    8,600 sq mi22k km² matching 80% or better

    For example Siocon (90), Recodo (84)

  10. 10

    🇧🇷Maranhão, Brazil

    7,800 sq mi20k km² matching 80% or better

    For example Zé Doca (85), Alto Alegre do Pindaré (83)

  11. 11

    🇮🇳Andaman and Nicobar, India

    6,400 sq mi17k km² matching 80% or better

    For example Rangat (91), Port Blair (86)

  12. 12

    🇧🇴Beni Department, Bolivia

    4,900 sq mi13k km² matching 80% or better

    For example San Borja (79), Trinidad (75)

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

    🇵🇭MamburaoMimaropa, Philippines

    Zone 13b · No frost · 0 chill hours · 91 in rainZone 13b · No frost · 0 chill hours · 2,306 mm rain

    Shares 17 of your 19 great-fit crops

    98%match
  2. 2

    🇵🇭SablayanMimaropa, Philippines

    Zone 13b · No frost · 0 chill hours · 101 in rainZone 13b · No frost · 0 chill hours · 2,568 mm rain

    Shares 17 of your 19 great-fit crops

    98%match
  3. 3

    🇵🇭El NidoMimaropa, Philippines

    Zone 13b · No frost · 0 chill hours · 97 in rainZone 13b · No frost · 0 chill hours · 2,474 mm rain

    Shares 19 of your 19 great-fit crops

    98%match
  4. 4

    🇵🇭San Jose del MonteCentral Luzon, Philippines

    Zone 13a · No frost · 0 chill hours · 114 in rainZone 13a · No frost · 0 chill hours · 2,890 mm rain

    Shares 16 of your 19 great-fit crops

    97%match
  5. 5

    🇵🇭RoxasMimaropa, Philippines

    Zone 13b · No frost · 0 chill hours · 77 in rainZone 13b · No frost · 0 chill hours · 1,968 mm rain

    Shares 18 of your 19 great-fit crops

    96%match
  6. 6

    🇵🇭CulionMimaropa, Philippines

    Zone 13b · No frost · 0 chill hours · 128 in rainZone 13b · No frost · 0 chill hours · 3,250 mm rain

    Shares 14 of your 19 great-fit crops

    96%match
  7. 7

    🇵🇭SipalayWestern Visayas, Philippines

    Zone 13b · No frost · 0 chill hours · 103 in rainZone 13b · No frost · 0 chill hours · 2,608 mm rain

    Shares 17 of your 19 great-fit crops

    95%match
  8. 8

    🇵🇭MansalayMimaropa, Philippines

    Zone 13b · No frost · 0 chill hours · 118 in rainZone 13b · No frost · 0 chill hours · 2,990 mm rain

    Shares 15 of your 19 great-fit crops

    95%match
  9. 9

    🇵🇭San JoseMimaropa, Philippines

    Zone 13b · No frost · 0 chill hours · 126 in rainZone 13b · No frost · 0 chill hours · 3,209 mm rain

    Shares 15 of your 19 great-fit crops

    93%match
  10. 10

    🇵🇭Angeles CityCentral Luzon, Philippines

    Zone 13a · No frost · 0 chill hours · 94 in rainZone 13a · No frost · 0 chill hours · 2,393 mm rain

    Shares 17 of your 19 great-fit crops

    93%match
  11. 11

    🇵🇭BayawanCentral Visayas, Philippines

    Zone 13b · No frost · 0 chill hours · 100 in rainZone 13b · No frost · 0 chill hours · 2,535 mm rain

    Shares 17 of your 19 great-fit crops

    93%match
  12. 12

    🇵🇭PaeteCalabarzon, Philippines

    Zone 13a · No frost · 0 chill hours · 94 in rainZone 13a · No frost · 0 chill hours · 2,385 mm rain

    Shares 19 of your 19 great-fit crops

    93%match
  13. 13

    🇵🇭Cabanatuan CityCentral Luzon, Philippines

    Zone 13a · No frost · 0 chill hours · 80 in rainZone 13a · No frost · 0 chill hours · 2,021 mm rain

    Shares 16 of your 19 great-fit crops

    92%match
  14. 14

    🇵🇭Mabalacat CityCentral Luzon, Philippines

    Zone 13a · No frost · 0 chill hours · 88 in rainZone 13a · No frost · 0 chill hours · 2,227 mm rain

    Shares 17 of your 19 great-fit crops

    92%match
  15. 15

    🇵🇭Puerto PrincesaMimaropa, Philippines

    Zone 13b · No frost · 0 chill hours · 63 in rainZone 13b · No frost · 0 chill hours · 1,596 mm rain

    Shares 18 of your 19 great-fit crops

    91%match
  16. 16

    🇮🇳RangatAndaman and Nicobar, India

    Zone 13a · No frost · 0 chill hours · 132 in rainZone 13a · No frost · 0 chill hours · 3,346 mm rain

    Shares 16 of your 19 great-fit crops

    91%match
  17. 17

    🇮🇩Tegal↻Seasons flipped. Central Java, Indonesia

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

    Shares 18 of your 19 great-fit crops

    90%match
  18. 18

    🇵🇭IloiloWestern Visayas, Philippines

    Zone 13b · No frost · 0 chill hours · 97 in rainZone 13b · No frost · 0 chill hours · 2,452 mm rain

    Shares 17 of your 19 great-fit crops

    90%match
  19. 19

    🇵🇭RosarioCalabarzon, Philippines

    Zone 13a · No frost · 0 chill hours · 84 in rainZone 13a · No frost · 0 chill hours · 2,139 mm rain

    Shares 19 of your 19 great-fit crops

    90%match
  20. 20

    🇵🇭AuroraCalabarzon, Philippines

    Zone 13a · No frost · 0 chill hours · 74 in rainZone 13a · No frost · 0 chill hours · 1,886 mm rain

    Shares 19 of your 19 great-fit crops

    90%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 General Emilio Aguinaldo: 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.