一种解决方法是在BigqueryOperator中设置provide_context=True，并使用xcom_push将查询结果存储到XCom中。然后，使用xcom_pull从XCom中检索结果。以下是代码示例：

from airflow import DAG
from airflow.operators import BigQueryOperator
from datetime import datetime
import time

default_args = {
    'owner': 'airflow',
    'depends_on_past': False,
    'start_date': datetime(2019, 6, 1),
    'retries': 1,
    'retry_delay': timedelta(minutes=1),
}

with DAG('bigquery_test', default_args=default_args, schedule_interval='@hourly') as dag:

    bq_query = BigQueryOperator(
        task_id='bq_query',
        bql='SELECT * FROM `..` WHERE ',
        use_legacy_sql=False,
        destination_dataset_table=':.',
        write_disposition='WRITE_TRUNCATE',
        provide_context=True,
        dag=dag)

    def execute_and_push(**kwargs):
        from google.cloud import bigquery
        query_job = bigquery.Client().query('SELECT COUNT(*) FROM `..`')
        result = query_job.result()
        count = result.fetchone()[0]
        kwargs['ti'].xcom_push(key='bq_count', value=count)

    bq_push = PythonOperator(
        task_id='bq_push',
        python_callable=execute_and_push,
        provide_context=True,
        dag=dag)

    def pull_and_print(**kwargs):
        count = kwargs['ti'].xcom_pull(key='bq_count')
        print(f'Total rows in new table = {count}')

    bq_pull = PythonOperator(
        task_id='bq_pull',
        python_callable=pull_and_print,
        provide_context=True,
        dag=dag)

    bq_query >> bq_push >> bq_pull

在这个示例中，我们从一个表中查询数据，并将结果存储在另一个表中。然后，在execute_and_push PythonOperator中，我们将结果存储在XCom中。