Gridded weather data relies on daily temperature and precipitation measures from sparsely distributed weather stations, which is interpolated across space to generate accurate weather measures in levels.

The Issue. Order of operation for spatial interpolation and transformation to an extreme weather measure matters. Post-transformation to extreme weather metrics following spatial interpolation of weather station data is the 'Conventional Approach' we see in thousands of published papers using the gridded weather data. Pre-transformation to extreme weather metrics prior to spatial interpolation is the 'New Approach' we propose.

Case study. Using exposure hours in extreme temperature bins, following Schlenker and Roberts (2009), and extreme moisture conditions using the SPI-index, we show significant differences in extreme weather metrics depending on which approach is used. We find that the New Approach we propose does a much better job at recovering ground truth, especially at the tails where, arguably, scientists are most interested in for measuring weather extremes. Finally, we show that our proposed New Approach generates differences in U.S. Corn Yield Response Function.

Using compliance data from water systems linked to sub-daily weather observations, I examine how temperature, precipitation, and soil moisture affect microbial and chemical contamination. Extreme heat increases disinfection byproducts and coliform detection, whereas precipitation intensity drives microbial risk. Antecedent soil moisture strongly mediates impacts; wet conditions reduce contamination while dry soils amplify subsequent risks. Small systems show higher weather sensitivity compared to large systems for microbial outcomes, indicating disparate climate vulnerabilities. The findings highlight that weather already degrades water quality, with impacts concentrated in resource-limited systems serving rural communities, requiring targeted adaptation as extremes intensify.

We examine how residential water conservation mandates create heterogeneous welfare effects across different household types. Using a panel of single-family monthly water use, lot size composition, and home values across California water agencies, we estimate group-specific demand elasticities and welfare losses from command-and-control mandates. Large-lot agencies are less price responsive than small-lot agencies, yet when assigned to strict conservation tiers they achieved larger absolute reductions. After the mandate, large-lot agencies showed no significant rebound, while small-lot agencies rebounded by about 7%. Welfare analysis indicates that large-lot agencies bore higher absolute costs, whereas small-lot agencies experienced greater losses as a share of their water bills. These findings highlight the distributional tradeoffs between effectiveness and equity in conservation mandates.