批量下载全国区县历史数据

需求：下载全国所有区县 2025 年逐日平均气温、降雨量数据。

历史数据模型：era5_seamless
支持的逐小时要素: era5历史数据
全部支持的逐日数据要素查看：逐日要素支持
全部支持的逐月数据要素查看：逐月要素支持
以 Python 代码为例，分四步完成：

---

第一步：获取全国区县 adcode 列表

平台提供现成的行政区划 CSV 文件，直接下载解析即可。

import io
import time
import zipfile
import requests
import pandas as pd

API_KEY = "your_api_key"
BASE_URL = "https://api.mirror-earth.com"

# 下载区县表
res = requests.get(f"https://open.mirror-earth.com/district.csv")
res.encoding = "utf-8"
df = pd.read_csv(io.StringIO(res.text))
adcodes = df["adcode"].astype(str).tolist()

print(f"共 {len(adcodes)} 个区县")

---

第二步：创建批量下载任务

headers = {
    "X-API-Key": API_KEY,
    "Content-Type": "application/json",
}

payload = {
    "domain": "era5_seamless",
    "hourly": [],
    "daily": ["temperature_2m_mean", "rain_sum"],
    "monthly": [],
    "time_range": {
        "type": "date_range",
        "start": "2025-01-01",
        "end": "2025-12-31",
    },
    "area": {
        "type": "point",
        "adcodes": adcodes,
    },
    "timezone": "Asia/Shanghai",
    "area_average": False,
}

res = requests.post(f"{BASE_URL}/api/tasks/export", json=payload, headers=headers)
res.raise_for_status()
result = res.json()

task_id = result["data"]["task_id"]
print(f"任务已创建，task_id: {task_id}")

---

第三步：轮询等待任务完成

output_file = None

while True:
    res = requests.get(f"{BASE_URL}/api/tasks/{task_id}/status", headers=headers)
    res.raise_for_status()
    data = res.json()["data"]

    status = data["status"]
    progress = data.get("progress_percent", 0)
    print(f"任务状态：{status}，进度：{progress}%")

    if status == "completed":
        output_file = data["output_file"]
        break
    elif status in ("failed", "cancelled"):
        raise RuntimeError(f"任务异常，状态：{status}")

    time.sleep(10)

---

第四步：下载并解压任务结果

download_url = f"{BASE_URL}/user_downloads/{output_file}"
res = requests.get(download_url, headers=headers, stream=True)
res.raise_for_status()

zip_path = "district_2025.zip"
with open(zip_path, "wb") as f:
    for chunk in res.iter_content(chunk_size=8192):
        f.write(chunk)

with zipfile.ZipFile(zip_path, "r") as z:
    z.extractall("district_2025")

print("下载完成，数据已解压到 district_2025/")

---

完整代码

import io
import time
import zipfile
import requests
import pandas as pd

API_KEY = "your_api_key"
BASE_URL = "https://api.mirror-earth.com"

headers = {
    "X-API-Key": API_KEY,
    "Content-Type": "application/json",
}

# ── 1. 获取区县 adcode 列表 ──────────────────────────────────────
res = requests.get("https://open.mirror-earth.com/district.csv")
res.encoding = "utf-8"
df = pd.read_csv(io.StringIO(res.text))
adcodes = df["adcode"].astype(str).tolist()
print(f"共 {len(adcodes)} 个区县")

# ── 2. 创建任务 ──────────────────────────────────────────────────
payload = {
    "domain": "era5_seamless",
    "hourly": [],
    "daily": ["temperature_2m_mean", "rain_sum"],
    "monthly": [],
    "time_range": {
        "type": "date_range",
        "start": "2025-01-01",
        "end": "2025-12-31",
    },
    "area": {
        "type": "point",
        "adcodes": adcodes,
    },
    "timezone": "Asia/Shanghai",
    "area_average": False,
}

res = requests.post(f"{BASE_URL}/api/tasks/export", json=payload, headers=headers)
res.raise_for_status()
task_id = res.json()["data"]["task_id"]
print(f"任务已创建，task_id: {task_id}")

# ── 3. 轮询任务进度 ──────────────────────────────────────────────
output_file = None
while True:
    res = requests.get(f"{BASE_URL}/api/tasks/{task_id}/status", headers=headers)
    res.raise_for_status()
    data = res.json()["data"]
    status = data["status"]
    progress = data.get("progress_percent", 0)
    print(f"任务状态：{status}，进度：{progress}%")

    if status == "completed":
        output_file = data["output_file"]
        break
    elif status in ("failed", "cancelled"):
        raise RuntimeError(f"任务异常，状态：{status}")
    time.sleep(10)

# ── 4. 下载并解压结果 ────────────────────────────────────────────
download_url = f"{BASE_URL}/user_downloads/{output_file}"
res = requests.get(download_url, headers=headers, stream=True)
res.raise_for_status()

zip_path = "district_2025.zip"
with open(zip_path, "wb") as f:
    for chunk in res.iter_content(chunk_size=8192):
        f.write(chunk)

with zipfile.ZipFile(zip_path, "r") as z:
    z.extractall("district_2025")

print("下载完成，数据已解压到 district_2025/")

---

行政区划表

CSV 字段说明：