High-resolution meteorological data are necessary to understand and predict climate-driven impacts on the structure and function of terrestrial ecosystems. However, the spatial resolution of climate reanalysis data and climate model outputs is often too coarse for studies at local/landscape scales. Additionally, climate model projections usually contain important biases, requiring the application of statistical corrections. Here we present ‘meteoland’, an R package that integrates several tools to facilitate the estimation of daily weather over landscapes, both under current and future conditions. The package contains functions: (1) to interpolate daily weather including topographic effects; and (2) to correct the biases of a given weather series (e.g., climate model outputs). We illustrate and validate the functions of the package using weather station data from Catalonia (NE Spain), re-analysis data and climate model outputs for a specific county. We conclude with a discussion of current limitations and potential improvements of the package.

高分辨率气象数据对于了解和预测气候驱动的对陆地生态系统结构和功能的影响是必要的。但是，气候再分析数据和气候模型输出的空间分辨率对于本地/景观尺度的研究而言往往过于粗糙。此外，气候模型预测通常包含重要的偏差，需要进行统计校正。在这里，我们介绍“ meteoland”，这是一个R软件包，其中集成了多种工具，可方便地估算当前和未来条件下景观的每日天气。该软件包包含以下功能：（1）插值包括地形影响在内的日常天气；（2）纠正给定天气序列（例如，气候模型输出）的偏差。我们使用来自加泰罗尼亚（西班牙东北部）的气象站数据，重新分析数据和特定县的气候模型输出来说明和验证程序包的功能。我们在最后讨论了该软件包的局限性和潜在的改进。